Sliding Mode Observer-Based Current Sensor Fault Reconstruction and Unknown Load Disturbance Estimation for PMSM Driven System

PubMed Central

Li, Xiangfei; Lin, Yuliang

2017-01-01

This paper proposes a new scheme of reconstructing current sensor faults and estimating unknown load disturbance for a permanent magnet synchronous motor (PMSM)-driven system. First, the original PMSM system is transformed into two subsystems; the first subsystem has unknown system load disturbances, which are unrelated to sensor faults, and the second subsystem has sensor faults, but is free from unknown load disturbances. Introducing a new state variable, the augmented subsystem that has sensor faults can be transformed into having actuator faults. Second, two sliding mode observers (SMOs) are designed: the unknown load disturbance is estimated by the first SMO in the subsystem, which has unknown load disturbance, and the sensor faults can be reconstructed using the second SMO in the augmented subsystem, which has sensor faults. The gains of the proposed SMOs and their stability analysis are developed via the solution of linear matrix inequality (LMI). Finally, the effectiveness of the proposed scheme was verified by simulations and experiments. The results demonstrate that the proposed scheme can reconstruct current sensor faults and estimate unknown load disturbance for the PMSM-driven system. PMID:29211017

A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles

PubMed Central

Xu, Jun; Wang, Jing; Li, Shiying; Cao, Binggang

2016-01-01

Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists. PMID:27548183

A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles.

PubMed

Xu, Jun; Wang, Jing; Li, Shiying; Cao, Binggang

2016-08-19

Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists.

Fault Diagnosis for Micro-Gas Turbine Engine Sensors via Wavelet Entropy

PubMed Central

Yu, Bing; Liu, Dongdong; Zhang, Tianhong

2011-01-01

Sensor fault diagnosis is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can’t be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor fault diagnosis can be performed by extracting features of the measured signals. This paper proposes a novel fault diagnosis method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a fault diagnosis method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of faults with different magnitudes are presented. The experimental results show that the proposed method for sensor fault diagnosis is efficient. PMID:22163734

Fault diagnosis for micro-gas turbine engine sensors via wavelet entropy.

PubMed

Yu, Bing; Liu, Dongdong; Zhang, Tianhong

2011-01-01

Sensor fault diagnosis is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can't be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor fault diagnosis can be performed by extracting features of the measured signals. This paper proposes a novel fault diagnosis method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a fault diagnosis method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of faults with different magnitudes are presented. The experimental results show that the proposed method for sensor fault diagnosis is efficient.

Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, Howard; Braun, James E.

This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment inmore » the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.« less

Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, Howard; Braun, James E.

2015-12-31

This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment inmore » the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.« less

Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme

NASA Technical Reports Server (NTRS)

Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong

2011-01-01

A method detects and isolates in-flight sensor, actuator, and component faults for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor fault or component fault, but not both, this method considers sensor fault, actuator fault, and component fault under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive fault diagnostic estimators for residual generation, and a residual evaluation module that includes adaptive thresholds and a Transferable Belief Model (TBM)-based residual evaluation scheme. By employing a nonlinear adaptive learning architecture, the developed approach is capable of directly dealing with nonlinear engine models and nonlinear faults without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-fault modes, including a subset of sensor/actuator/components faults, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated faults as long as the fault magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred

A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

NASA Technical Reports Server (NTRS)

Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong; Farfan-Ramos, Luis; Simon, Donald L.

2010-01-01

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented.

Optimal Sensor Allocation for Fault Detection and Isolation

NASA Technical Reports Server (NTRS)

Azam, Mohammad; Pattipati, Krishna; Patterson-Hine, Ann

2004-01-01

Automatic fault diagnostic schemes rely on various types of sensors (e.g., temperature, pressure, vibration, etc) to measure the system parameters. Efficacy of a diagnostic scheme is largely dependent on the amount and quality of information available from these sensors. The reliability of sensors, as well as the weight, volume, power, and cost constraints, often makes it impractical to monitor a large number of system parameters. An optimized sensor allocation that maximizes the fault diagnosibility, subject to specified weight, volume, power, and cost constraints is required. Use of optimal sensor allocation strategies during the design phase can ensure better diagnostics at a reduced cost for a system incorporating a high degree of built-in testing. In this paper, we propose an approach that employs multiple fault diagnosis (MFD) and optimization techniques for optimal sensor placement for fault detection and isolation (FDI) in complex systems. Keywords: sensor allocation, multiple fault diagnosis, Lagrangian relaxation, approximate belief revision, multidimensional knapsack problem.

Machine learning techniques for fault isolation and sensor placement

NASA Technical Reports Server (NTRS)

Carnes, James R.; Fisher, Douglas H.

1993-01-01

Fault isolation and sensor placement are vital for monitoring and diagnosis. A sensor conveys information about a system's state that guides troubleshooting if problems arise. We are using machine learning methods to uncover behavioral patterns over snapshots of system simulations that will aid fault isolation and sensor placement, with an eye towards minimality, fault coverage, and noise tolerance.

Robust Fault Detection and Isolation for Stochastic Systems

NASA Technical Reports Server (NTRS)

George, Jemin; Gregory, Irene M.

2010-01-01

This paper outlines the formulation of a robust fault detection and isolation scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves precise reconstruction of sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the robust fault detection and isolation system.

Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2004-01-01

In this paper, an approach for in-flight fault detection and isolation (FDI) of aircraft engine sensors based on a bank of Kalman filters is developed. This approach utilizes multiple Kalman filters, each of which is designed based on a specific fault hypothesis. When the propulsion system experiences a fault, only one Kalman filter with the correct hypothesis is able to maintain the nominal estimation performance. Based on this knowledge, the isolation of faults is achieved. Since the propulsion system may experience component and actuator faults as well, a sensor FDI system must be robust in terms of avoiding misclassifications of any anomalies. The proposed approach utilizes a bank of (m+1) Kalman filters where m is the number of sensors being monitored. One Kalman filter is used for the detection of component and actuator faults while each of the other m filters detects a fault in a specific sensor. With this setup, the overall robustness of the sensor FDI system to anomalies is enhanced. Moreover, numerous component fault events can be accounted for by the FDI system. The sensor FDI system is applied to a commercial aircraft engine simulation, and its performance is evaluated at multiple power settings at a cruise operating point using various fault scenarios.

A comparative study of sensor fault diagnosis methods based on observer for ECAS system

NASA Astrophysics Data System (ADS)

Xu, Xing; Wang, Wei; Zou, Nannan; Chen, Long; Cui, Xiaoli

2017-03-01

The performance and practicality of electronically controlled air suspension (ECAS) system are highly dependent on the state information supplied by kinds of sensors, but faults of sensors occur frequently. Based on a non-linearized 3-DOF 1/4 vehicle model, different methods of fault detection and isolation (FDI) are used to diagnose the sensor faults for ECAS system. The considered approaches include an extended Kalman filter (EKF) with concise algorithm, a strong tracking filter (STF) with robust tracking ability, and the cubature Kalman filter (CKF) with numerical precision. We propose three filters of EKF, STF, and CKF to design a state observer of ECAS system under typical sensor faults and noise. Results show that three approaches can successfully detect and isolate faults respectively despite of the existence of environmental noise, FDI time delay and fault sensitivity of different algorithms are different, meanwhile, compared with EKF and STF, CKF method has best performing FDI of sensor faults for ECAS system.

A wideband magnetoresistive sensor for monitoring dynamic fault slip in laboratory fault friction experiments

USGS Publications Warehouse

Kilgore, Brian D.

2017-01-01

A non-contact, wideband method of sensing dynamic fault slip in laboratory geophysical experiments employs an inexpensive magnetoresistive sensor, a small neodymium rare earth magnet, and user built application-specific wideband signal conditioning. The magnetoresistive sensor generates a voltage proportional to the changing angles of magnetic flux lines, generated by differential motion or rotation of the near-by magnet, through the sensor. The performance of an array of these sensors compares favorably to other conventional position sensing methods employed at multiple locations along a 2 m long × 0.4 m deep laboratory strike-slip fault. For these magnetoresistive sensors, the lack of resonance signals commonly encountered with cantilever-type position sensor mounting, the wide band response (DC to ≈ 100 kHz) that exceeds the capabilities of many traditional position sensors, and the small space required on the sample, make them attractive options for capturing high speed fault slip measurements in these laboratory experiments. An unanticipated observation of this study is the apparent sensitivity of this sensor to high frequency electomagnetic signals associated with fault rupture and (or) rupture propagation, which may offer new insights into the physics of earthquake faulting.

Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

PubMed Central

Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

2016-01-01

Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods. PMID:26797611

Sensor fault-tolerant control for gear-shifting engaging process of automated manual transmission

NASA Astrophysics Data System (ADS)

Li, Liang; He, Kai; Wang, Xiangyu; Liu, Yahui

2018-01-01

Angular displacement sensor on the actuator of automated manual transmission (AMT) is sensitive to fault, and the sensor fault will disturb its normal control, which affects the entire gear-shifting process of AMT and results in awful riding comfort. In order to solve this problem, this paper proposes a method of fault-tolerant control for AMT gear-shifting engaging process. By using the measured current of actuator motor and angular displacement of actuator, the gear-shifting engaging load torque table is built and updated before the occurrence of the sensor fault. Meanwhile, residual between estimated and measured angular displacements is used to detect the sensor fault. Once the residual exceeds a determined fault threshold, the sensor fault is detected. Then, switch control is triggered, and the current observer and load torque table estimates an actual gear-shifting position to replace the measured one to continue controlling the gear-shifting process. Numerical and experiment tests are carried out to evaluate the reliability and feasibility of proposed methods, and the results show that the performance of estimation and control is satisfactory.

An Indirect Adaptive Control Scheme in the Presence of Actuator and Sensor Failures

NASA Technical Reports Server (NTRS)

Sun, Joy Z.; Josh, Suresh M.

2009-01-01

The problem of controlling a system in the presence of unknown actuator and sensor faults is addressed. The system is assumed to have groups of actuators, and groups of sensors, with each group consisting of multiple redundant similar actuators or sensors. The types of actuator faults considered consist of unknown actuators stuck in unknown positions, as well as reduced actuator effectiveness. The sensor faults considered include unknown biases and outages. The approach employed for fault detection and estimation consists of a bank of Kalman filters based on multiple models, and subsequent control reconfiguration to mitigate the effect of biases caused by failed components as well as to obtain stability and satisfactory performance using the remaining actuators and sensors. Conditions for fault identifiability are presented, and the adaptive scheme is applied to an aircraft flight control example in the presence of actuator failures. Simulation results demonstrate that the method can rapidly and accurately detect faults and estimate the fault values, thus enabling safe operation and acceptable performance in spite of failures.

Learning and diagnosing faults using neural networks

NASA Technical Reports Server (NTRS)

Whitehead, Bruce A.; Kiech, Earl L.; Ali, Moonis

1990-01-01

Neural networks have been employed for learning fault behavior from rocket engine simulator parameters and for diagnosing faults on the basis of the learned behavior. Two problems in applying neural networks to learning and diagnosing faults are (1) the complexity of the sensor data to fault mapping to be modeled by the neural network, which implies difficult and lengthy training procedures; and (2) the lack of sufficient training data to adequately represent the very large number of different types of faults which might occur. Methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Secondly, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented during training. These methods were tested on a task of fault diagnosis by employing rocket engine simulator data. Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

PubMed Central

Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

2013-01-01

Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study. PMID:24351636

Reliability of measured data for pH sensor arrays with fault diagnosis and data fusion based on LabVIEW.

PubMed

Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

2013-12-13

Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

NASA Technical Reports Server (NTRS)

2004-01-01

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

Nuclear Power Plant Thermocouple Sensor-Fault Detection and Classification Using Deep Learning and Generalized Likelihood Ratio Test

NASA Astrophysics Data System (ADS)

Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.

2017-06-01

In this paper, an online fault detection and classification method is proposed for thermocouples used in nuclear power plants. In the proposed method, the fault data are detected by the classification method, which classifies the fault data from the normal data. Deep belief network (DBN), a technique for deep learning, is applied to classify the fault data. The DBN has a multilayer feature extraction scheme, which is highly sensitive to a small variation of data. Since the classification method is unable to detect the faulty sensor; therefore, a technique is proposed to identify the faulty sensor from the fault data. Finally, the composite statistical hypothesis test, namely generalized likelihood ratio test, is applied to compute the fault pattern of the faulty sensor signal based on the magnitude of the fault. The performance of the proposed method is validated by field data obtained from thermocouple sensors of the fast breeder test reactor.

Distributed adaptive diagnosis of sensor faults using structural response data

NASA Astrophysics Data System (ADS)

Dragos, Kosmas; Smarsly, Kay

2016-10-01

The reliability and consistency of wireless structural health monitoring (SHM) systems can be compromised by sensor faults, leading to miscalibrations, corrupted data, or even data loss. Several research approaches towards fault diagnosis, referred to as ‘analytical redundancy’, have been proposed that analyze the correlations between different sensor outputs. In wireless SHM, most analytical redundancy approaches require centralized data storage on a server for data analysis, while other approaches exploit the on-board computing capabilities of wireless sensor nodes, analyzing the raw sensor data directly on board. However, using raw sensor data poses an operational constraint due to the limited power resources of wireless sensor nodes. In this paper, a new distributed autonomous approach towards sensor fault diagnosis based on processed structural response data is presented. The inherent correlations among Fourier amplitudes of acceleration response data, at peaks corresponding to the eigenfrequencies of the structure, are used for diagnosis of abnormal sensor outputs at a given structural condition. Representing an entirely data-driven analytical redundancy approach that does not require any a priori knowledge of the monitored structure or of the SHM system, artificial neural networks (ANN) are embedded into the sensor nodes enabling cooperative fault diagnosis in a fully decentralized manner. The distributed analytical redundancy approach is implemented into a wireless SHM system and validated in laboratory experiments, demonstrating the ability of wireless sensor nodes to self-diagnose sensor faults accurately and efficiently with minimal data traffic. Besides enabling distributed autonomous fault diagnosis, the embedded ANNs are able to adapt to the actual condition of the structure, thus ensuring accurate and efficient fault diagnosis even in case of structural changes.

Improved Sensor Fault Detection, Isolation, and Mitigation Using Multiple Observers Approach

PubMed Central

Wang, Zheng; Anand, D. M.; Moyne, J.; Tilbury, D. M.

2017-01-01

Traditional Fault Detection and Isolation (FDI) methods analyze a residual signal to detect and isolate sensor faults. The residual signal is the difference between the sensor measurements and the estimated outputs of the system based on an observer. The traditional residual-based FDI methods, however, have some limitations. First, they require that the observer has reached its steady state. In addition, residual-based methods may not detect some sensor faults, such as faults on critical sensors that result in an unobservable system. Furthermore, the system may be in jeopardy if actions required for mitigating the impact of the faulty sensors are not taken before the faulty sensors are identified. The contribution of this paper is to propose three new methods to address these limitations. Faults that occur during the observers' transient state can be detected by analyzing the convergence rate of the estimation error. Open-loop observers, which do not rely on sensor information, are used to detect faults on critical sensors. By switching among different observers, we can potentially mitigate the impact of the faulty sensor during the FDI process. These three methods are systematically integrated with a previously developed residual-based method to provide an improved FDI and mitigation capability framework. The overall approach is validated mathematically, and the effectiveness of the overall approach is demonstrated through simulation on a 5-state suspension system. PMID:28924303

Simultaneous Sensor and Process Fault Diagnostics for Propellant Feed System

NASA Technical Reports Server (NTRS)

Cao, J.; Kwan, C.; Figueroa, F.; Xu, R.

2006-01-01

The main objective of this research is to extract fault features from sensor faults and process faults by using advanced fault detection and isolation (FDI) algorithms. A tank system that has some common characteristics to a NASA testbed at Stennis Space Center was used to verify our proposed algorithms. First, a generic tank system was modeled. Second, a mathematical model suitable for FDI has been derived for the tank system. Third, a new and general FDI procedure has been designed to distinguish process faults and sensor faults. Extensive simulations clearly demonstrated the advantages of the new design.

Sensor fault detection and recovery in satellite attitude control

NASA Astrophysics Data System (ADS)

Nasrolahi, Seiied Saeed; Abdollahi, Farzaneh

2018-04-01

This paper proposes an integrated sensor fault detection and recovery for the satellite attitude control system. By introducing a nonlinear observer, the healthy sensor measurements are provided. Considering attitude dynamics and kinematic, a novel observer is developed to detect the fault in angular rate as well as attitude sensors individually or simultaneously. There is no limit on type and configuration of attitude sensors. By designing a state feedback based control signal and Lyapunov stability criterion, the uniformly ultimately boundedness of tracking errors in the presence of sensor faults is guaranteed. Finally, simulation results are presented to illustrate the performance of the integrated scheme.

Adaptive sensor-fault tolerant control for a class of multivariable uncertain nonlinear systems.

PubMed

Khebbache, Hicham; Tadjine, Mohamed; Labiod, Salim; Boulkroune, Abdesselem

2015-03-01

This paper deals with the active fault tolerant control (AFTC) problem for a class of multiple-input multiple-output (MIMO) uncertain nonlinear systems subject to sensor faults and external disturbances. The proposed AFTC method can tolerate three additive (bias, drift and loss of accuracy) and one multiplicative (loss of effectiveness) sensor faults. By employing backstepping technique, a novel adaptive backstepping-based AFTC scheme is developed using the fact that sensor faults and system uncertainties (including external disturbances and unexpected nonlinear functions caused by sensor faults) can be on-line estimated and compensated via robust adaptive schemes. The stability analysis of the closed-loop system is rigorously proven using a Lyapunov approach. The effectiveness of the proposed controller is illustrated by two simulation examples. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Integral Sensor Fault Detection and Isolation for Railway Traction Drive.

PubMed

Garramiola, Fernando; Del Olmo, Jon; Poza, Javier; Madina, Patxi; Almandoz, Gaizka

2018-05-13

Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of faults has become an important key for Railway traction drive manufacturers. Sensor faults are important sources of failures. Among the different fault diagnosis approaches, in this article an integral diagnosis strategy for sensors in traction drives is presented. Such strategy is composed of an observer-based approach for direct current (DC)-link voltage and catenary current sensors, a frequency analysis approach for motor current phase sensors and a hardware redundancy solution for speed sensors. None of them requires any hardware change requirement in the actual traction drive. All the fault detection and isolation approaches have been validated in a Hardware-in-the-loop platform comprising a Real Time Simulator and a commercial Traction Control Unit for a tram. In comparison to safety-critical systems in Aerospace applications, Railway applications do not need instantaneous detection, and the diagnosis is validated in a short time period for reliable decision. Combining the different approaches and existing hardware redundancy, an integral fault diagnosis solution is provided, to detect and isolate faults in all the sensors installed in the traction drive.

Integral Sensor Fault Detection and Isolation for Railway Traction Drive

PubMed Central

del Olmo, Jon; Poza, Javier; Madina, Patxi; Almandoz, Gaizka

2018-01-01

Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of faults has become an important key for Railway traction drive manufacturers. Sensor faults are important sources of failures. Among the different fault diagnosis approaches, in this article an integral diagnosis strategy for sensors in traction drives is presented. Such strategy is composed of an observer-based approach for direct current (DC)-link voltage and catenary current sensors, a frequency analysis approach for motor current phase sensors and a hardware redundancy solution for speed sensors. None of them requires any hardware change requirement in the actual traction drive. All the fault detection and isolation approaches have been validated in a Hardware-in-the-loop platform comprising a Real Time Simulator and a commercial Traction Control Unit for a tram. In comparison to safety-critical systems in Aerospace applications, Railway applications do not need instantaneous detection, and the diagnosis is validated in a short time period for reliable decision. Combining the different approaches and existing hardware redundancy, an integral fault diagnosis solution is provided, to detect and isolate faults in all the sensors installed in the traction drive. PMID:29757251

Sensor Fault Detection and Diagnosis Simulation of a Helicopter Engine in an Intelligent Control Framework

NASA Technical Reports Server (NTRS)

Litt, Jonathan; Kurtkaya, Mehmet; Duyar, Ahmet

1994-01-01

This paper presents an application of a fault detection and diagnosis scheme for the sensor faults of a helicopter engine. The scheme utilizes a model-based approach with real time identification and hypothesis testing which can provide early detection, isolation, and diagnosis of failures. It is an integral part of a proposed intelligent control system with health monitoring capabilities. The intelligent control system will allow for accommodation of faults, reduce maintenance cost, and increase system availability. The scheme compares the measured outputs of the engine with the expected outputs of an engine whose sensor suite is functioning normally. If the differences between the real and expected outputs exceed threshold values, a fault is detected. The isolation of sensor failures is accomplished through a fault parameter isolation technique where parameters which model the faulty process are calculated on-line with a real-time multivariable parameter estimation algorithm. The fault parameters and their patterns can then be analyzed for diagnostic and accommodation purposes. The scheme is applied to the detection and diagnosis of sensor faults of a T700 turboshaft engine. Sensor failures are induced in a T700 nonlinear performance simulation and data obtained are used with the scheme to detect, isolate, and estimate the magnitude of the faults.

A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

PubMed

Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

2017-09-29

The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

Modeling the Fault Tolerant Capability of a Flight Control System: An Exercise in SCR Specification

NASA Technical Reports Server (NTRS)

Alexander, Chris; Cortellessa, Vittorio; DelGobbo, Diego; Mili, Ali; Napolitano, Marcello

2000-01-01

In life-critical and mission-critical applications, it is important to make provisions for a wide range of contingencies, by providing means for fault tolerance. In this paper, we discuss the specification of a flight control system that is fault tolerant with respect to sensor faults. Redundancy is provided by analytical relations that hold between sensor readings; depending on the conditions, this redundancy can be used to detect, identify and accommodate sensor faults.

Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.

PubMed

Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

2017-03-09

Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.

Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence

PubMed Central

Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

2017-01-01

Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults. PMID:28282936

Fault detection and isolation in motion monitoring system.

PubMed

Kim, Duk-Jin; Suk, Myoung Hoon; Prabhakaran, B

2012-01-01

Pervasive computing becomes very active research field these days. A watch that can trace human movement to record motion boundary as well as to study of finding social life pattern by one's localized visiting area. Pervasive computing also helps patient monitoring. A daily monitoring system helps longitudinal study of patient monitoring such as Alzheimer's and Parkinson's or obesity monitoring. Due to the nature of monitoring sensor (on-body wireless sensor), however, signal noise or faulty sensors errors can be present at any time. Many research works have addressed these problems any with a large amount of sensor deployment. In this paper, we present the faulty sensor detection and isolation using only two on-body sensors. We have been investigating three different types of sensor errors: the SHORT error, the CONSTANT error, and the NOISY SENSOR error (see more details on section V). Our experimental results show that the success rate of isolating faulty signals are an average of over 91.5% on fault type 1, over 92% on fault type 2, and over 99% on fault type 3 with the fault prior of 30% sensor errors.

Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles.

PubMed

Jeon, Namju; Lee, Hyeongcheol

2016-12-12

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed.

Enhanced Bank of Kalman Filters Developed and Demonstrated for In-Flight Aircraft Engine Sensor Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2005-01-01

In-flight sensor fault detection and isolation (FDI) is critical to maintaining reliable engine operation during flight. The aircraft engine control system, which computes control commands on the basis of sensor measurements, operates the propulsion systems at the demanded conditions. Any undetected sensor faults, therefore, may cause the control system to drive the engine into an undesirable operating condition. It is critical to detect and isolate failed sensors as soon as possible so that such scenarios can be avoided. A challenging issue in developing reliable sensor FDI systems is to make them robust to changes in engine operating characteristics due to degradation with usage and other faults that can occur during flight. A sensor FDI system that cannot appropriately account for such scenarios may result in false alarms, missed detections, or misclassifications when such faults do occur. To address this issue, an enhanced bank of Kalman filters was developed, and its performance and robustness were demonstrated in a simulation environment. The bank of filters is composed of m + 1 Kalman filters, where m is the number of sensors being used by the control system and, thus, in need of monitoring. Each Kalman filter is designed on the basis of a unique fault hypothesis so that it will be able to maintain its performance if a particular fault scenario, hypothesized by that particular filter, takes place.

Solar system fault detection

DOEpatents

Farrington, R.B.; Pruett, J.C. Jr.

1984-05-14

A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

Solar system fault detection

DOEpatents

Farrington, Robert B.; Pruett, Jr., James C.

1986-01-01

A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

Robust In-Flight Sensor Fault Diagnostics for Aircraft Engine Based on Sliding Mode Observers

PubMed Central

Chang, Xiaodong; Huang, Jinquan; Lu, Feng

2017-01-01

For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme for a commercial aircraft engine based on a sliding mode observer (SMO). In this approach, one sliding mode observer is designed for engine health performance tracking, and another for sensor fault reconstruction. Both observers are employed in in-flight applications. The results of the former SMO are analyzed for post-flight updating the baseline model of the latter. This idea is practical and feasible since the updating process does not require the algorithm to be regulated or redesigned, so that ground-based intervention is avoided, and the update process is implemented in an economical and efficient way. With this setup, the robustness of the proposed scheme to the health degradation is much enhanced and the latter SMO is able to fulfill sensor fault reconstruction over the course of the engine life. The proposed sensor fault diagnostic system is applied to a nonlinear simulation of a commercial aircraft engine, and its effectiveness is evaluated in several fault scenarios. PMID:28398255

Robust In-Flight Sensor Fault Diagnostics for Aircraft Engine Based on Sliding Mode Observers.

PubMed

Chang, Xiaodong; Huang, Jinquan; Lu, Feng

2017-04-11

For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme for a commercial aircraft engine based on a sliding mode observer (SMO). In this approach, one sliding mode observer is designed for engine health performance tracking, and another for sensor fault reconstruction. Both observers are employed in in-flight applications. The results of the former SMO are analyzed for post-flight updating the baseline model of the latter. This idea is practical and feasible since the updating process does not require the algorithm to be regulated or redesigned, so that ground-based intervention is avoided, and the update process is implemented in an economical and efficient way. With this setup, the robustness of the proposed scheme to the health degradation is much enhanced and the latter SMO is able to fulfill sensor fault reconstruction over the course of the engine life. The proposed sensor fault diagnostic system is applied to a nonlinear simulation of a commercial aircraft engine, and its effectiveness is evaluated in several fault scenarios.

Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures.

PubMed

Bounemeur, Abdelhamid; Chemachema, Mohamed; Essounbouli, Najib

2018-05-10

In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Smart Sensor for Online Detection of Multiple-Combined Faults in VSD-Fed Induction Motors

PubMed Central

Garcia-Ramirez, Armando G.; Osornio-Rios, Roque A.; Granados-Lieberman, David; Garcia-Perez, Arturo; Romero-Troncoso, Rene J.

2012-01-01

Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can be produce severe damages. The combined fault identification in induction motors is a demanding task, but it has been rarely considered in spite of being a common situation, because it is difficult to identify two or more faults simultaneously. This work presents a smart sensor for online detection of simple and multiple-combined faults in induction motors fed through a VSD in a wide frequency range covering low frequencies from 3 Hz and high frequencies up to 60 Hz based on a primary sensor being a commercially available current clamp or a hall-effect sensor. The proposed smart sensor implements a methodology based on the fast Fourier transform (FFT), RMS calculation and artificial neural networks (ANN), which are processed online using digital hardware signal processing based on field programmable gate array (FPGA).

Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles

PubMed Central

Jeon, Namju; Lee, Hyeongcheol

2016-01-01

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed. PMID:27973431

Distributed fault detection over sensor networks with Markovian switching topologies

NASA Astrophysics Data System (ADS)

Ge, Xiaohua; Han, Qing-Long

2014-05-01

This paper deals with the distributed fault detection for discrete-time Markov jump linear systems over sensor networks with Markovian switching topologies. The sensors are scatteredly deployed in the sensor field and the fault detectors are physically distributed via a communication network. The system dynamics changes and sensing topology variations are modeled by a discrete-time Markov chain with incomplete mode transition probabilities. Each of these sensor nodes firstly collects measurement outputs from its all underlying neighboring nodes, processes these data in accordance with the Markovian switching topologies, and then transmits the processed data to the remote fault detector node. Network-induced delays and accumulated data packet dropouts are incorporated in the data transmission between the sensor nodes and the distributed fault detector nodes through the communication network. To generate localized residual signals, mode-independent distributed fault detection filters are proposed. By means of the stochastic Lyapunov functional approach, the residual system performance analysis is carried out such that the overall residual system is stochastically stable and the error between each residual signal and the fault signal is made as small as possible. Furthermore, a sufficient condition on the existence of the mode-independent distributed fault detection filters is derived in the simultaneous presence of incomplete mode transition probabilities, Markovian switching topologies, network-induced delays, and accumulated data packed dropouts. Finally, a stirred-tank reactor system is given to show the effectiveness of the developed theoretical results.

Current Sensor Fault Reconstruction for PMSM Drives

PubMed Central

Huang, Gang; Luo, Yi-Ping; Zhang, Chang-Fan; He, Jing; Huang, Yi-Shan

2016-01-01

This paper deals with a current sensor fault reconstruction algorithm for the torque closed-loop drive system of an interior PMSM. First, sensor faults are equated to actuator ones by a new introduced state variable. Then, in αβ coordinates, based on the motor model with active flux linkage, a current observer is constructed with a specific sliding mode equivalent control methodology to eliminate the effects of unknown disturbances, and the phase current sensor faults are reconstructed by means of an adaptive method. Finally, an αβ axis current fault processing module is designed based on the reconstructed value. The feasibility and effectiveness of the proposed method are verified by simulation and experimental tests on the RT-LAB platform. PMID:26840317

Sensor fault diagnosis of aero-engine based on divided flight status.

PubMed

Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

2017-11-01

Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

Sensor fault diagnosis of aero-engine based on divided flight status

NASA Astrophysics Data System (ADS)

Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

2017-11-01

Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

Experimental Robot Position Sensor Fault Tolerance Using Accelerometers and Joint Torque Sensors

NASA Technical Reports Server (NTRS)

Aldridge, Hal A.; Juang, Jer-Nan

1997-01-01

Robot systems in critical applications, such as those in space and nuclear environments, must be able to operate during component failure to complete important tasks. One failure mode that has received little attention is the failure of joint position sensors. Current fault tolerant designs require the addition of directly redundant position sensors which can affect joint design. The proposed method uses joint torque sensors found in most existing advanced robot designs along with easily locatable, lightweight accelerometers to provide a joint position sensor fault recovery mode. This mode uses the torque sensors along with a virtual passive control law for stability and accelerometers for joint position information. Two methods for conversion from Cartesian acceleration to joint position based on robot kinematics, not integration, are presented. The fault tolerant control method was tested on several joints of a laboratory robot. The controllers performed well with noisy, biased data and a model with uncertain parameters.

Evaluation of passenger health risk assessment of sustainable indoor air quality monitoring in metro systems based on a non-Gaussian dynamic sensor validation method.

PubMed

Kim, MinJeong; Liu, Hongbin; Kim, Jeong Tai; Yoo, ChangKyoo

2014-08-15

Sensor faults in metro systems provide incorrect information to indoor air quality (IAQ) ventilation systems, resulting in the miss-operation of ventilation systems and adverse effects on passenger health. In this study, a new sensor validation method is proposed to (1) detect, identify and repair sensor faults and (2) evaluate the influence of sensor reliability on passenger health risk. To address the dynamic non-Gaussianity problem of IAQ data, dynamic independent component analysis (DICA) is used. To detect and identify sensor faults, the DICA-based squared prediction error and sensor validity index are used, respectively. To restore the faults to normal measurements, a DICA-based iterative reconstruction algorithm is proposed. The comprehensive indoor air-quality index (CIAI) that evaluates the influence of the current IAQ on passenger health is then compared using the faulty and reconstructed IAQ data sets. Experimental results from a metro station showed that the DICA-based method can produce an improved IAQ level in the metro station and reduce passenger health risk since it more accurately validates sensor faults than do conventional methods. Copyright © 2014 Elsevier B.V. All rights reserved.

A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite's attitude control subsystem.

PubMed

Talebi, H A; Khorasani, K; Tafazoli, S

2009-01-01

This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2003-01-01

In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.

A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

NASA Astrophysics Data System (ADS)

Kim, Pyung Soo

2017-04-01

In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite measurements and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noisefree systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, extensive simulations are performed for the discretized DC motor system with two types of sensor faults, incipient soft bias-type fault and abrupt bias-type fault. In particular, according to diverse noise levels and windows lengths, meaningful simulation results are given for the abrupt bias-type fault.

A fault isolation method based on the incidence matrix of an augmented system

NASA Astrophysics Data System (ADS)

Chen, Changxiong; Chen, Liping; Ding, Jianwan; Wu, Yizhong

2018-03-01

A new approach is proposed for isolating faults and fast identifying the redundant sensors of a system in this paper. By introducing fault signal as additional state variable, an augmented system model is constructed by the original system model, fault signals and sensor measurement equations. The structural properties of an augmented system model are provided in this paper. From the viewpoint of evaluating fault variables, the calculating correlations of the fault variables in the system can be found, which imply the fault isolation properties of the system. Compared with previous isolation approaches, the highlights of the new approach are that it can quickly find the faults which can be isolated using exclusive residuals, at the same time, and can identify the redundant sensors in the system, which are useful for the design of diagnosis system. The simulation of a four-tank system is reported to validate the proposed method.

Identifiability of Additive, Time-Varying Actuator and Sensor Faults by State Augmentation

NASA Technical Reports Server (NTRS)

Upchurch, Jason M.; Gonzalez, Oscar R.; Joshi, Suresh M.

2014-01-01

Recent work has provided a set of necessary and sucient conditions for identifiability of additive step faults (e.g., lock-in-place actuator faults, constant bias in the sensors) using state augmentation. This paper extends these results to an important class of faults which may affect linear, time-invariant systems. In particular, the faults under consideration are those which vary with time and affect the system dynamics additively. Such faults may manifest themselves in aircraft as, for example, control surface oscillations, control surface runaway, and sensor drift. The set of necessary and sucient conditions presented in this paper are general, and apply when a class of time-varying faults affects arbitrary combinations of actuators and sensors. The results in the main theorems are illustrated by two case studies, which provide some insight into how the conditions may be used to check the theoretical identifiability of fault configurations of interest for a given system. It is shown that while state augmentation can be used to identify certain fault configurations, other fault configurations are theoretically impossible to identify using state augmentation, giving practitioners valuable insight into such situations. That is, the limitations of state augmentation for a given system and configuration of faults are made explicit. Another limitation of model-based methods is that there can be large numbers of fault configurations, thus making identification of all possible configurations impractical. However, the theoretical identifiability of known, credible fault configurations can be tested using the theorems presented in this paper, which can then assist the efforts of fault identification practitioners.

Fault tolerant multi-sensor fusion based on the information gain

NASA Astrophysics Data System (ADS)

Hage, Joelle Al; El Najjar, Maan E.; Pomorski, Denis

2017-01-01

In the last decade, multi-robot systems are used in several applications like for example, the army, the intervention areas presenting danger to human life, the management of natural disasters, the environmental monitoring, exploration and agriculture. The integrity of localization of the robots must be ensured in order to achieve their mission in the best conditions. Robots are equipped with proprioceptive (encoders, gyroscope) and exteroceptive sensors (Kinect). However, these sensors could be affected by various faults types that can be assimilated to erroneous measurements, bias, outliers, drifts,… In absence of a sensor fault diagnosis step, the integrity and the continuity of the localization are affected. In this work, we present a muti-sensors fusion approach with Fault Detection and Exclusion (FDE) based on the information theory. In this context, we are interested by the information gain given by an observation which may be relevant when dealing with the fault tolerance aspect. Moreover, threshold optimization based on the quantity of information given by a decision on the true hypothesis is highlighted.

Fault detection, isolation, and diagnosis of self-validating multifunctional sensors.

PubMed

Yang, Jing-Li; Chen, Yin-Sheng; Zhang, Li-Li; Sun, Zhen

2016-06-01

A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

Fiber Bragg grating sensor for fault detection in high voltage overhead transmission lines

NASA Astrophysics Data System (ADS)

Moghadas, Amin

2011-12-01

A fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by fiber Bragg grating (FBG) sensors. The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signals. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG sensors and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system.

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

PubMed Central

Lv, Yong; Song, Gangbing

2018-01-01

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal. PMID:29659510

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.

PubMed

Yuan, Rui; Lv, Yong; Song, Gangbing

2018-04-16

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

Sensor placement for diagnosability in space-borne systems - A model-based reasoning approach

NASA Technical Reports Server (NTRS)

Chien, Steve; Doyle, Richard; Rouquette, Nicolas

1992-01-01

This paper presents an approach to evaluating sensor placements on the basis of how well they are able to discriminate between a given fault and normal operating modes and/or other fault modes. In this approach, a model of the system in both normal operations and fault modes is used to evaluate possible sensor placements upon the basis of three criteria. Discriminability measures how much of a divergence in expected sensor readings the two system modes can be expected to produce. Accuracy measures confidence in the particular model predictions. Timeliness measures how long after the fault occurrence the expected divergence will take place. These three metrics then can be used to form a recommendation for a sensor placement. This paper describes how these measures can be computed and illustrated these methods with a brief example.

Optimal Sensor Location Design for Reliable Fault Detection in Presence of False Alarms

PubMed Central

Yang, Fan; Xiao, Deyun; Shah, Sirish L.

2009-01-01

To improve fault detection reliability, sensor location should be designed according to an optimization criterion with constraints imposed by issues of detectability and identifiability. Reliability requires the minimization of undetectability and false alarm probability due to random factors on sensor readings, which is not only related with sensor readings but also affected by fault propagation. This paper introduces the reliability criteria expression based on the missed/false alarm probability of each sensor and system topology or connectivity derived from the directed graph. The algorithm for the optimization problem is presented as a heuristic procedure. Finally, a boiler system is illustrated using the proposed method. PMID:22291524

Analytic Confusion Matrix Bounds for Fault Detection and Isolation Using a Sum-of-Squared- Residuals Approach

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2009-01-01

Given a system which can fail in 1 or n different ways, a fault detection and isolation (FDI) algorithm uses sensor data in order to determine which fault is the most likely to have occurred. The effectiveness of an FDI algorithm can be quantified by a confusion matrix, which i ndicates the probability that each fault is isolated given that each fault has occurred. Confusion matrices are often generated with simulation data, particularly for complex systems. In this paper we perform FDI using sums of squares of sensor residuals (SSRs). We assume that the sensor residuals are Gaussian, which gives the SSRs a chi-squared distribution. We then generate analytic lower and upper bounds on the confusion matrix elements. This allows for the generation of optimal sensor sets without numerical simulations. The confusion matrix bound s are verified with simulated aircraft engine data.

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of a Baseline System

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2008-01-01

In this paper, a baseline system which utilizes dual-channel sensor measurements for aircraft engine on-line diagnostics is developed. This system is composed of a linear on-board engine model (LOBEM) and fault detection and isolation (FDI) logic. The LOBEM provides the analytical third channel against which the dual-channel measurements are compared. When the discrepancy among the triplex channels exceeds a tolerance level, the FDI logic determines the cause of the discrepancy. Through this approach, the baseline system achieves the following objectives: (1) anomaly detection, (2) component fault detection, and (3) sensor fault detection and isolation. The performance of the baseline system is evaluated in a simulation environment using faults in sensors and components.

A Novel Dual Separate Paths (DSP) Algorithm Providing Fault-Tolerant Communication for Wireless Sensor Networks.

PubMed

Tien, Nguyen Xuan; Kim, Semog; Rhee, Jong Myung; Park, Sang Yoon

2017-07-25

Fault tolerance has long been a major concern for sensor communications in fault-tolerant cyber physical systems (CPSs). Network failure problems often occur in wireless sensor networks (WSNs) due to various factors such as the insufficient power of sensor nodes, the dislocation of sensor nodes, the unstable state of wireless links, and unpredictable environmental interference. Fault tolerance is thus one of the key requirements for data communications in WSN applications. This paper proposes a novel path redundancy-based algorithm, called dual separate paths (DSP), that provides fault-tolerant communication with the improvement of the network traffic performance for WSN applications, such as fault-tolerant CPSs. The proposed DSP algorithm establishes two separate paths between a source and a destination in a network based on the network topology information. These paths are node-disjoint paths and have optimal path distances. Unicast frames are delivered from the source to the destination in the network through the dual paths, providing fault-tolerant communication and reducing redundant unicast traffic for the network. The DSP algorithm can be applied to wired and wireless networks, such as WSNs, to provide seamless fault-tolerant communication for mission-critical and life-critical applications such as fault-tolerant CPSs. The analyzed and simulated results show that the DSP-based approach not only provides fault-tolerant communication, but also improves network traffic performance. For the case study in this paper, when the DSP algorithm was applied to high-availability seamless redundancy (HSR) networks, the proposed DSP-based approach reduced the network traffic by 80% to 88% compared with the standard HSR protocol, thus improving network traffic performance.

An uncertainty-based distributed fault detection mechanism for wireless sensor networks.

PubMed

Yang, Yang; Gao, Zhipeng; Zhou, Hang; Qiu, Xuesong

2014-04-25

Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio.

Analysis of field-oriented controlled induction motor drives under sensor faults and an overview of sensorless schemes.

PubMed

Arun Dominic, D; Chelliah, Thanga Raj

2014-09-01

To obtain high dynamic performance on induction motor drives (IMD), variable voltage and variable frequency operation has to be performed by measuring speed of rotation and stator currents through sensors and fed back them to the controllers. When the sensors are undergone a fault, the stability of control system, may be designed for an industrial process, is disturbed. This paper studies the negative effects on a 12.5 hp induction motor drives when the field oriented control system is subjected to sensor faults. To illustrate the importance of this study mine hoist load diagram is considered as shaft load of the tested machine. The methods to recover the system from sensor faults are discussed. In addition, the various speed sensorless schemes are reviewed comprehensively. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Simple Random Sampling-Based Probe Station Selection for Fault Detection in Wireless Sensor Networks

PubMed Central

Huang, Rimao; Qiu, Xuesong; Rui, Lanlan

2011-01-01

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate. PMID:22163789

Simple random sampling-based probe station selection for fault detection in wireless sensor networks.

PubMed

Huang, Rimao; Qiu, Xuesong; Rui, Lanlan

2011-01-01

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate.

Mechanical fault detection of electric motors by laser vibrometer and accelerometer measurements

NASA Astrophysics Data System (ADS)

Cristalli, C.; Paone, N.; Rodríguez, R. M.

2006-08-01

This paper presents a comparative study between accelerometer and laser vibrometer measurements aimed at on-line quality control carried out on the universal motors used in washing machines, which exhibit defects localised mainly in the bearings, including faults in the cage, in the rolling element and in the outer and inner ring. A set of no defective and defective motors were analysed by means of the acceleration signal provided by the accelerometer, and the displacement and velocity signals given by a single-point laser vibrometer. Advantages and disadvantages of both absolute and relative sensors and of contact and non-contact instrumentation are discussed taking into account the applicability to real on-line quality control measurements and bringing to light the related measurement problems due to the specific environmental conditions of assembly lines and sensor installation constraints. The performance of different signal-processing algorithms is discussed: RMS computation at steady-state proves effective for pass or fail diagnosis, while the amplitude of selected frequencies in the averaged spectra allows also for classification of a variety of special faults in bearings. Joint time-frequency analysis output data can be successfully used for pass or fail diagnosis during transients, thus achieving a remarkable reduction in testing time, which is important for on-line diagnostics.

An Uncertainty-Based Distributed Fault Detection Mechanism for Wireless Sensor Networks

PubMed Central

Yang, Yang; Gao, Zhipeng; Zhou, Hang; Qiu, Xuesong

2014-01-01

Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio. PMID:24776937

Seismic anisotropy in the vicinity of the Alpine fault, New Zealand, estimated by seismic interferometry

NASA Astrophysics Data System (ADS)

Takagi, R.; Okada, T.; Yoshida, K.; Townend, J.; Boese, C. M.; Baratin, L. M.; Chamberlain, C. J.; Savage, M. K.

2016-12-01

We estimate shear wave velocity anisotropy in shallow crust near the Alpine fault using seismic interferometry of borehole vertical arrays. We utilized four borehole observations: two sensors are deployed in two boreholes of the Deep Fault Drilling Project in the hanging wall side, and the other two sites are located in the footwall side. Surface sensors deployed just above each borehole are used to make vertical arrays. Crosscorrelating rotated horizontal seismograms observed by the borehole and surface sensors, we extracted polarized shear waves propagating from the bottom to the surface of each borehole. The extracted shear waves show polarization angle dependence of travel time, indicating shear wave anisotropy between the two sensors. In the hanging wall side, the estimated fast shear wave directions are parallel to the Alpine fault. Strong anisotropy of 20% is observed at the site within 100 m from the Alpine fault. The hanging wall consists of mylonite and schist characterized by fault parallel foliation. In addition, an acoustic borehole imaging reveals fractures parallel to the Alpine fault. The fault parallel anisotropy suggest structural anisotropy is predominant in the hanging wall, demonstrating consistency of geological and seismological observations. In the footwall side, on the other hand, the angle between the fast direction and the strike of the Alpine fault is 33-40 degrees. Since the footwall is composed of granitoid that may not have planar structure, stress induced anisotropy is possibly predominant. The direction of maximum horizontal stress (SHmax) estimated by focal mechanisms of regional earthquakes is 55 degrees of the Alpine fault. Possible interpretation of the difference between the fast direction and SHmax direction is depth rotation of stress field near the Alpine fault. Similar depth rotation of stress field is also observed in the SAFOD borehole at the San Andreas fault.

Sensor fault diagnosis of singular delayed LPV systems with inexact parameters: an uncertain system approach

NASA Astrophysics Data System (ADS)

Hassanabadi, Amir Hossein; Shafiee, Masoud; Puig, Vicenc

2018-01-01

In this paper, sensor fault diagnosis of a singular delayed linear parameter varying (LPV) system is considered. In the considered system, the model matrices are dependent on some parameters which are real-time measurable. The case of inexact parameter measurements is considered which is close to real situations. Fault diagnosis in this system is achieved via fault estimation. For this purpose, an augmented system is created by including sensor faults as additional system states. Then, an unknown input observer (UIO) is designed which estimates both the system states and the faults in the presence of measurement noise, disturbances and uncertainty induced by inexact measured parameters. Error dynamics and the original system constitute an uncertain system due to inconsistencies between real and measured values of the parameters. Then, the robust estimation of the system states and the faults are achieved with H∞ performance and formulated with a set of linear matrix inequalities (LMIs). The designed UIO is also applicable for fault diagnosis of singular delayed LPV systems with unmeasurable scheduling variables. The efficiency of the proposed approach is illustrated with an example.

Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis

PubMed Central

Jiang, Wen; Xie, Chunhe; Zhuang, Miaoyan; Shou, Yehang; Tang, Yongchuan

2016-01-01

Sensor data fusion technology is widely employed in fault diagnosis. The information in a sensor data fusion system is characterized by not only fuzziness, but also partial reliability. Uncertain information of sensors, including randomness, fuzziness, etc., has been extensively studied recently. However, the reliability of a sensor is often overlooked or cannot be analyzed adequately. A Z-number, Z = (A, B), can represent the fuzziness and the reliability of information simultaneously, where the first component A represents a fuzzy restriction on the values of uncertain variables and the second component B is a measure of the reliability of A. In order to model and process the uncertainties in a sensor data fusion system reasonably, in this paper, a novel method combining the Z-number and Dempster–Shafer (D-S) evidence theory is proposed, where the Z-number is used to model the fuzziness and reliability of the sensor data and the D-S evidence theory is used to fuse the uncertain information of Z-numbers. The main advantages of the proposed method are that it provides a more robust measure of reliability to the sensor data, and the complementary information of multi-sensors reduces the uncertainty of the fault recognition, thus enhancing the reliability of fault detection. PMID:27649193

Method and system for diagnostics of apparatus

NASA Technical Reports Server (NTRS)

Gorinevsky, Dimitry (Inventor)

2012-01-01

Proposed is a method, implemented in software, for estimating fault state of an apparatus outfitted with sensors. At each execution period the method processes sensor data from the apparatus to obtain a set of parity parameters, which are further used for estimating fault state. The estimation method formulates a convex optimization problem for each fault hypothesis and employs a convex solver to compute fault parameter estimates and fault likelihoods for each fault hypothesis. The highest likelihoods and corresponding parameter estimates are transmitted to a display device or an automated decision and control system. The obtained accurate estimate of fault state can be used to improve safety, performance, or maintenance processes for the apparatus.

Fault detection and isolation for complex system

NASA Astrophysics Data System (ADS)

Jing, Chan Shi; Bayuaji, Luhur; Samad, R.; Mustafa, M.; Abdullah, N. R. H.; Zain, Z. M.; Pebrianti, Dwi

2017-07-01

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

NASA Technical Reports Server (NTRS)

Sowers, T. Shane; Kopasakis, George; Simon, Donald L.

2008-01-01

The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance, there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight, and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

NASA Technical Reports Server (NTRS)

Sowers, T. Shane; Kopasakis, George; Simon, Donald L.

2008-01-01

The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

Simultaneous Event-Triggered Fault Detection and Estimation for Stochastic Systems Subject to Deception Attacks.

PubMed

Li, Yunji; Wu, QingE; Peng, Li

2018-01-23

In this paper, a synthesized design of fault-detection filter and fault estimator is considered for a class of discrete-time stochastic systems in the framework of event-triggered transmission scheme subject to unknown disturbances and deception attacks. A random variable obeying the Bernoulli distribution is employed to characterize the phenomena of the randomly occurring deception attacks. To achieve a fault-detection residual is only sensitive to faults while robust to disturbances, a coordinate transformation approach is exploited. This approach can transform the considered system into two subsystems and the unknown disturbances are removed from one of the subsystems. The gain of fault-detection filter is derived by minimizing an upper bound of filter error covariance. Meanwhile, system faults can be reconstructed by the remote fault estimator. An recursive approach is developed to obtain fault estimator gains as well as guarantee the fault estimator performance. Furthermore, the corresponding event-triggered sensor data transmission scheme is also presented for improving working-life of the wireless sensor node when measurement information are aperiodically transmitted. Finally, a scaled version of an industrial system consisting of local PC, remote estimator and wireless sensor node is used to experimentally evaluate the proposed theoretical results. In particular, a novel fault-alarming strategy is proposed so that the real-time capacity of fault-detection is guaranteed when the event condition is triggered.

Health Monitoring of a Satellite System

NASA Technical Reports Server (NTRS)

Chen, Robert H.; Ng, Hok K.; Speyer, Jason L.; Guntur, Lokeshkumar S.; Carpenter, Russell

2004-01-01

A health monitoring system based on analytical redundancy is developed for satellites on elliptical orbits. First, the dynamics of the satellite including orbital mechanics and attitude dynamics is modelled as a periodic system. Then, periodic fault detection filters are designed to detect and identify the satellite's actuator and sensor faults. In addition, parity equations are constructed using the algebraic redundant relationship among the actuators and sensors. Furthermore, a residual processor is designed to generate the probability of each of the actuator and sensor faults by using a sequential probability test. Finally, the health monitoring system, consisting of periodic fault detection lters, parity equations and residual processor, is evaluated in the simulation in the presence of disturbances and uncertainty.

Vehicle Fault Diagnose Based on Smart Sensor

NASA Astrophysics Data System (ADS)

Zhining, Li; Peng, Wang; Jianmin, Mei; Jianwei, Li; Fei, Teng

In the vehicle's traditional fault diagnose system, we usually use a computer system with a A/D card and with many sensors connected to it. The disadvantage of this system is that these sensor can hardly be shared with control system and other systems, there are too many connect lines and the electro magnetic compatibility(EMC) will be affected. In this paper, smart speed sensor, smart acoustic press sensor, smart oil press sensor, smart acceleration sensor and smart order tracking sensor were designed to solve this problem. With the CAN BUS these smart sensors, fault diagnose computer and other computer could be connected together to establish a network system which can monitor and control the vehicle's diesel and other system without any duplicate sensor. The hard and soft ware of the smart sensor system was introduced, the oil press, vibration and acoustic signal are resampled by constant angle increment to eliminate the influence of the rotate speed. After the resample, the signal in every working cycle could be averaged in angle domain and do other analysis like order spectrum.

Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems

PubMed Central

Huang, Gang; Luo, Yi-Ping; Zhang, Chang-Fan; Huang, Yi-Shan; Zhao, Kai-Hui

2015-01-01

This paper proposes a current sensor fault detection method based on a sliding mode observer for the torque closed-loop control system of interior permanent magnet synchronous motors. First, a sliding mode observer based on the extended flux linkage is built to simplify the motor model, which effectively eliminates the phenomenon of salient poles and the dependence on the direct axis inductance parameter, and can also be used for real-time calculation of feedback torque. Then a sliding mode current observer is constructed in αβ coordinates to generate the fault residuals of the phase current sensors. The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple. The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop. The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method. PMID:25970258

A General theory of Signal Integration for Fault-Tolerant Dynamic Distributed Sensor Networks

DTIC Science & Technology

1993-10-01

related to a) the architecture and fault- tolerance of the distributed sensor network, b) the proper synchronisation of sensor signals, c) the...Computational complexities of the problem of distributed detection. 5) Issues related to recording of events and synchronization in distributed sensor...Intervals for Synchronization in Real Time Distributed Systems", Submitted to Electronic Encyclopedia. 3. V. G. Hegde and S. S. Iyengar "Efficient

A Fault Tolerant System for an Integrated Avionics Sensor Configuration

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Lancraft, R. E.

1984-01-01

An aircraft sensor fault tolerant system methodology for the Transport Systems Research Vehicle in a Microwave Landing System (MLS) environment is described. The fault tolerant system provides reliable estimates in the presence of possible failures both in ground-based navigation aids, and in on-board flight control and inertial sensors. Sensor failures are identified by utilizing the analytic relationships between the various sensors arising from the aircraft point mass equations of motion. The estimation and failure detection performance of the software implementation (called FINDS) of the developed system was analyzed on a nonlinear digital simulation of the research aircraft. Simulation results showing the detection performance of FINDS, using a dual redundant sensor compliment, are presented for bias, hardover, null, ramp, increased noise and scale factor failures. In general, the results show that FINDS can distinguish between normal operating sensor errors and failures while providing an excellent detection speed for bias failures in the MLS, indicated airspeed, attitude and radar altimeter sensors.

Optimal design of the absolute positioning sensor for a high-speed maglev train and research on its fault diagnosis.

PubMed

Zhang, Dapeng; Long, Zhiqiang; Xue, Song; Zhang, Junge

2012-01-01

This paper studies an absolute positioning sensor for a high-speed maglev train and its fault diagnosis method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the fault characters, and the signal flow method is used to locate the faulty parts. The diagnosis information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line diagnosis for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project.

Real time health monitoring and control system methodology for flexible space structures

NASA Astrophysics Data System (ADS)

Jayaram, Sanjay

This dissertation is concerned with the Near Real-time Autonomous Health Monitoring of Flexible Space Structures. The dynamics of multi-body flexible systems is uncertain due to factors such as high non-linearity, consideration of higher modal frequencies, high dimensionality, multiple inputs and outputs, operational constraints, as well as unexpected failures of sensors and/or actuators. Hence a systematic framework of developing a high fidelity, dynamic model of a flexible structural system needs to be understood. The fault detection mechanism that will be an integrated part of an autonomous health monitoring system comprises the detection of abnormalities in the sensors and/or actuators and correcting these detected faults (if possible). Applying the robust control law and the robust measures that are capable of detecting and recovering/replacing the actuators rectifies the actuator faults. The fault tolerant concept applied to the sensors will be in the form of an Extended Kalman Filter (EKF). The EKF is going to weigh the information coming from multiple sensors (redundant sensors used to measure the same information) and automatically identify the faulty sensors and weigh the best estimate from the remaining sensors. The mechanization is comprised of instrumenting flexible deployable panels (solar array) with multiple angular position and rate sensors connected to the data acquisition system. The sensors will give position and rate information of the solar panel in all three axes (i.e. roll, pitch and yaw). The position data corresponds to the steady state response and the rate data will give better insight on the transient response of the system. This is a critical factor for real-time autonomous health monitoring. MATLAB (and/or C++) software will be used for high fidelity modeling and fault tolerant mechanism.

Fault detection and diagnosis in a spacecraft attitude determination system

NASA Astrophysics Data System (ADS)

Pirmoradi, F. N.; Sassani, F.; de Silva, C. W.

2009-09-01

This paper presents a new scheme for fault detection and diagnosis (FDD) in spacecraft attitude determination (AD) sensors. An integrated attitude determination system, which includes measurements of rate and angular position using rate gyros and vector sensors, is developed. Measurement data from all sensors are fused by a linearized Kalman filter, which is designed based on the system kinematics, to provide attitude estimation and the values of the gyro bias. Using this information the erroneous sensor measurements are corrected, and unbounded sensor measurement errors are avoided. The resulting bias-free data are used in the FDD scheme. The FDD algorithm uses model-based state estimation, combining the information from the rotational dynamics and kinematics of a spacecraft with the sensor measurements to predict the future sensor outputs. Fault isolation is performed through extended Kalman filters (EKFs). The innovation sequences of EKFs are monitored by several statistical tests to detect the presence of a failure and to localize the failures in all AD sensors. The isolation procedure is developed in two phases. In the first phase, two EKFs are designed, which use subsets of measurements to provide state estimates and form residuals, which are used to verify the source of the fault. In the second phase of isolation, testing of multiple hypotheses is performed. The generalized likelihood ratio test is utilized to identify the faulty components. In the scheme developed in this paper a relatively small number of hypotheses is used, which results in faster isolation and highly distinguishable fault signatures. An important feature of the developed FDD scheme is that it can provide attitude estimations even if only one type of sensors is functioning properly.

Test experience on an ultrareliable computer communication network

NASA Technical Reports Server (NTRS)

Abbott, L. W.

1984-01-01

The dispersed sensor processing mesh (DSPM) is an experimental, ultrareliable, fault-tolerant computer communications network that exhibits an organic-like ability to regenerate itself after suffering damage. The regeneration is accomplished by two routines - grow and repair. This paper discusses the DSPM concept for achieving fault tolerance and provides a brief description of the mechanization of both the experiment and the six-node experimental network. The main topic of this paper is the system performance of the growth algorithm contained in the grow routine. The characteristics imbued to DSPM by the growth algorithm are also discussed. Data from an experimental DSPM network and software simulation of larger DSPM-type networks are used to examine the inherent limitation on growth time by the growth algorithm and the relationship of growth time to network size and topology.

Fault Isolation Filter for Networked Control System with Event-Triggered Sampling Scheme

PubMed Central

Li, Shanbin; Sauter, Dominique; Xu, Bugong

2011-01-01

In this paper, the sensor data is transmitted only when the absolute value of difference between the current sensor value and the previously transmitted one is greater than the given threshold value. Based on this send-on-delta scheme which is one of the event-triggered sampling strategies, a modified fault isolation filter for a discrete-time networked control system with multiple faults is then implemented by a particular form of the Kalman filter. The proposed fault isolation filter improves the resource utilization with graceful fault estimation performance degradation. An illustrative example is given to show the efficiency of the proposed method. PMID:22346590

Cable-fault locator

NASA Technical Reports Server (NTRS)

Cason, R. L.; Mcstay, J. J.; Heymann, A. P., Sr.

1979-01-01

Inexpensive system automatically indicates location of short-circuited section of power cable. Monitor does not require that cable be disconnected from its power source or that test signals be applied. Instead, ground-current sensors are installed in manholes or at other selected locations along cable run. When fault occurs, sensors transmit information about fault location to control center. Repair crew can be sent to location and cable can be returned to service with minimum of downtime.

Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis

PubMed Central

He, Qingbo; Wang, Xiangxiang; Zhou, Qiang

2014-01-01

Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods. PMID:24379045

Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions.

PubMed

Hu, Yue; Tu, Xiaotong; Li, Fucai; Meng, Guang

2018-01-07

Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults.

Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions

PubMed Central

Li, Fucai; Meng, Guang

2018-01-01

Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults. PMID:29316668

Fault diagnosis of sensor networked structures with multiple faults using a virtual beam based approach

NASA Astrophysics Data System (ADS)

Wang, H.; Jing, X. J.

2017-07-01

This paper presents a virtual beam based approach suitable for conducting diagnosis of multiple faults in complex structures with limited prior knowledge of the faults involved. The "virtual beam", a recently-proposed concept for fault detection in complex structures, is applied, which consists of a chain of sensors representing a vibration energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for fault detection due to limited prior knowledge of normal operational conditions and fault conditions. To isolate the multiple faults within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple fault localization with limited prior knowledge of normal conditions and faults. With extensive experimental results, it is validated that the proposed method can localize both single fault and multiple faults more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.

Optimal Design of the Absolute Positioning Sensor for a High-Speed Maglev Train and Research on Its Fault Diagnosis

PubMed Central

Zhang, Dapeng; Long, Zhiqiang; Xue, Song; Zhang, Junge

2012-01-01

This paper studies an absolute positioning sensor for a high-speed maglev train and its fault diagnosis method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the fault characters, and the signal flow method is used to locate the faulty parts. The diagnosis information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line diagnosis for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project. PMID:23112619

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network

PubMed Central

Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

2015-01-01

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information. PMID:25938760

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

PubMed

Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

2015-01-01

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of an Enhanced System

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2008-01-01

In this paper, an enhanced on-line diagnostic system which utilizes dual-channel sensor measurements is developed for the aircraft engine application. The enhanced system is composed of a nonlinear on-board engine model (NOBEM), the hybrid Kalman filter (HKF) algorithm, and fault detection and isolation (FDI) logic. The NOBEM provides the analytical third channel against which the dual-channel measurements are compared. The NOBEM is further utilized as part of the HKF algorithm which estimates measured engine parameters. Engine parameters obtained from the dual-channel measurements, the NOBEM, and the HKF are compared against each other. When the discrepancy among the signals exceeds a tolerance level, the FDI logic determines the cause of discrepancy. Through this approach, the enhanced system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the enhanced system is evaluated in a simulation environment using faults in sensors and components, and it is compared to an existing baseline system.

Reliability and availability evaluation of Wireless Sensor Networks for industrial applications.

PubMed

Silva, Ivanovitch; Guedes, Luiz Affonso; Portugal, Paulo; Vasques, Francisco

2012-01-01

Wireless Sensor Networks (WSN) currently represent the best candidate to be adopted as the communication solution for the last mile connection in process control and monitoring applications in industrial environments. Most of these applications have stringent dependability (reliability and availability) requirements, as a system failure may result in economic losses, put people in danger or lead to environmental damages. Among the different type of faults that can lead to a system failure, permanent faults on network devices have a major impact. They can hamper communications over long periods of time and consequently disturb, or even disable, control algorithms. The lack of a structured approach enabling the evaluation of permanent faults, prevents system designers to optimize decisions that minimize these occurrences. In this work we propose a methodology based on an automatic generation of a fault tree to evaluate the reliability and availability of Wireless Sensor Networks, when permanent faults occur on network devices. The proposal supports any topology, different levels of redundancy, network reconfigurations, criticality of devices and arbitrary failure conditions. The proposed methodology is particularly suitable for the design and validation of Wireless Sensor Networks when trying to optimize its reliability and availability requirements.

Reliability and Availability Evaluation of Wireless Sensor Networks for Industrial Applications

PubMed Central

Silva, Ivanovitch; Guedes, Luiz Affonso; Portugal, Paulo; Vasques, Francisco

2012-01-01

Wireless Sensor Networks (WSN) currently represent the best candidate to be adopted as the communication solution for the last mile connection in process control and monitoring applications in industrial environments. Most of these applications have stringent dependability (reliability and availability) requirements, as a system failure may result in economic losses, put people in danger or lead to environmental damages. Among the different type of faults that can lead to a system failure, permanent faults on network devices have a major impact. They can hamper communications over long periods of time and consequently disturb, or even disable, control algorithms. The lack of a structured approach enabling the evaluation of permanent faults, prevents system designers to optimize decisions that minimize these occurrences. In this work we propose a methodology based on an automatic generation of a fault tree to evaluate the reliability and availability of Wireless Sensor Networks, when permanent faults occur on network devices. The proposal supports any topology, different levels of redundancy, network reconfigurations, criticality of devices and arbitrary failure conditions. The proposed methodology is particularly suitable for the design and validation of Wireless Sensor Networks when trying to optimize its reliability and availability requirements. PMID:22368497

A sensor network based virtual beam-like structure method for fault diagnosis and monitoring of complex structures with Improved Bacterial Optimization

NASA Astrophysics Data System (ADS)

Wang, H.; Jing, X. J.

2017-02-01

This paper proposes a novel method for the fault diagnosis of complex structures based on an optimized virtual beam-like structure approach. A complex structure can be regarded as a combination of numerous virtual beam-like structures considering the vibration transmission path from vibration sources to each sensor. The structural 'virtual beam' consists of a sensor chain automatically obtained by an Improved Bacterial Optimization Algorithm (IBOA). The biologically inspired optimization method (i.e. IBOA) is proposed for solving the discrete optimization problem associated with the selection of the optimal virtual beam for fault diagnosis. This novel virtual beam-like-structure approach needs less or little prior knowledge. Neither does it require stationary response data, nor is it confined to a specific structure design. It is easy to implement within a sensor network attached to the monitored structure. The proposed fault diagnosis method has been tested on the detection of loosening screws located at varying positions in a real satellite-like model. Compared with empirical methods, the proposed virtual beam-like structure method has proved to be very effective and more reliable for fault localization.

Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

PubMed

Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

2017-03-01

A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals

PubMed Central

Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue

2013-01-01

Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox. PMID:23334609

Evaluation of MEMS-Based Wireless Accelerometer Sensors in Detecting Gear Tooth Faults in Helicopter Transmissions

NASA Technical Reports Server (NTRS)

Lewicki, David George; Lambert, Nicholas A.; Wagoner, Robert S.

2015-01-01

The diagnostics capability of micro-electro-mechanical systems (MEMS) based rotating accelerometer sensors in detecting gear tooth crack failures in helicopter main-rotor transmissions was evaluated. MEMS sensors were installed on a pre-notched OH-58C spiral-bevel pinion gear. Endurance tests were performed and the gear was run to tooth fracture failure. Results from the MEMS sensor were compared to conventional accelerometers mounted on the transmission housing. Most of the four stationary accelerometers mounted on the gear box housing and most of the CI's used gave indications of failure at the end of the test. The MEMS system performed well and lasted the entire test. All MEMS accelerometers gave an indication of failure at the end of the test. The MEMS systems performed as well, if not better, than the stationary accelerometers mounted on the gear box housing with regards to gear tooth fault detection. For both the MEMS sensors and stationary sensors, the fault detection time was not much sooner than the actual tooth fracture time. The MEMS sensor spectrum data showed large first order shaft frequency sidebands due to the measurement rotating frame of reference. The method of constructing a pseudo tach signal from periodic characteristics of the vibration data was successful in deriving a TSA signal without an actual tach and proved as an effective way to improve fault detection for the MEMS.

An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

NASA Astrophysics Data System (ADS)

Liu, Jie; Hu, Youmin; Wang, Yan; Wu, Bo; Fan, Jikai; Hu, Zhongxu

2018-05-01

The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.

Test experience on an ultrareliable computer communication network

NASA Technical Reports Server (NTRS)

Abbott, L. W.

1984-01-01

The dispersed sensor processing mesh (DSPM) is an experimental, ultra-reliable, fault-tolerant computer communications network that exhibits an organic-like ability to regenerate itself after suffering damage. The regeneration is accomplished by two routines - grow and repair. This paper discusses the DSPM concept for achieving fault tolerance and provides a brief description of the mechanization of both the experiment and the six-node experimental network. The main topic of this paper is the system performance of the growth algorithm contained in the grow routine. The characteristics imbued to DSPM by the growth algorithm are also discussed. Data from an experimental DSPM network and software simulation of larger DSPM-type networks are used to examine the inherent limitation on growth time by the growth algorithm and the relationship of growth time to network size and topology.

Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

PubMed Central

Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

2016-01-01

Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods. PMID:27754386

Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network.

PubMed

Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

2016-10-13

Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

Research on Mechanical Fault Prediction Algorithm for Circuit Breaker Based on Sliding Time Window and ANN

NASA Astrophysics Data System (ADS)

Wang, Xiaohua; Rong, Mingzhe; Qiu, Juan; Liu, Dingxin; Su, Biao; Wu, Yi

A new type of algorithm for predicting the mechanical faults of a vacuum circuit breaker (VCB) based on an artificial neural network (ANN) is proposed in this paper. There are two types of mechanical faults in a VCB: operation mechanism faults and tripping circuit faults. An angle displacement sensor is used to measure the main axle angle displacement which reflects the displacement of the moving contact, to obtain the state of the operation mechanism in the VCB, while a Hall current sensor is used to measure the trip coil current, which reflects the operation state of the tripping circuit. Then an ANN prediction algorithm based on a sliding time window is proposed in this paper and successfully used to predict mechanical faults in a VCB. The research results in this paper provide a theoretical basis for the realization of online monitoring and fault diagnosis of a VCB.

AGSM Functional Fault Models for Fault Isolation Project

NASA Technical Reports Server (NTRS)

Harp, Janicce Leshay

2014-01-01

This project implements functional fault models to automate the isolation of failures during ground systems operations. FFMs will also be used to recommend sensor placement to improve fault isolation capabilities. The project enables the delivery of system health advisories to ground system operators.

A signal-based fault detection and classification method for heavy haul wagons

NASA Astrophysics Data System (ADS)

Li, Chunsheng; Luo, Shihui; Cole, Colin; Spiryagin, Maksym; Sun, Yanquan

2017-12-01

This paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.

Robust Fault Detection for Aircraft Using Mixed Structured Singular Value Theory and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Collins, Emmanuel G.

2000-01-01

The purpose of fault detection is to identify when a fault or failure has occurred in a system such as an aircraft or expendable launch vehicle. The faults may occur in sensors, actuators, structural components, etc. One of the primary approaches to model-based fault detection relies on analytical redundancy. That is the output of a computer-based model (actually a state estimator) is compared with the sensor measurements of the actual system to determine when a fault has occurred. Unfortunately, the state estimator is based on an idealized mathematical description of the underlying plant that is never totally accurate. As a result of these modeling errors, false alarms can occur. This research uses mixed structured singular value theory, a relatively recent and powerful robustness analysis tool, to develop robust estimators and demonstrates the use of these estimators in fault detection. To allow qualitative human experience to be effectively incorporated into the detection process fuzzy logic is used to predict the seriousness of the fault that has occurred.

Fault tolerant operation of switched reluctance machine

NASA Astrophysics Data System (ADS)

Wang, Wei

The energy crisis and environmental challenges have driven industry towards more energy efficient solutions. With nearly 60% of electricity consumed by various electric machines in industry sector, advancement in the efficiency of the electric drive system is of vital importance. Adjustable speed drive system (ASDS) provides excellent speed regulation and dynamic performance as well as dramatically improved system efficiency compared with conventional motors without electronics drives. Industry has witnessed tremendous grow in ASDS applications not only as a driving force but also as an electric auxiliary system for replacing bulky and low efficiency auxiliary hydraulic and mechanical systems. With the vast penetration of ASDS, its fault tolerant operation capability is more widely recognized as an important feature of drive performance especially for aerospace, automotive applications and other industrial drive applications demanding high reliability. The Switched Reluctance Machine (SRM), a low cost, highly reliable electric machine with fault tolerant operation capability, has drawn substantial attention in the past three decades. Nevertheless, SRM is not free of fault. Certain faults such as converter faults, sensor faults, winding shorts, eccentricity and position sensor faults are commonly shared among all ASDS. In this dissertation, a thorough understanding of various faults and their influence on transient and steady state performance of SRM is developed via simulation and experimental study, providing necessary knowledge for fault detection and post fault management. Lumped parameter models are established for fast real time simulation and drive control. Based on the behavior of the faults, a fault detection scheme is developed for the purpose of fast and reliable fault diagnosis. In order to improve the SRM power and torque capacity under faults, the maximum torque per ampere excitation are conceptualized and validated through theoretical analysis and experiments. With the proposed optimal waveform, torque production is greatly improved under the same Root Mean Square (RMS) current constraint. Additionally, position sensorless operation methods under phase faults are investigated to account for the combination of physical position sensor and phase winding faults. A comprehensive solution for position sensorless operation under single and multiple phases fault are proposed and validated through experiments. Continuous position sensorless operation with seamless transition between various numbers of phase fault is achieved.

Multi-Unmanned Aerial Vehicle (UAV) Cooperative Fault Detection Employing Differential Global Positioning (DGPS), Inertial and Vision Sensors.

PubMed

Heredia, Guillermo; Caballero, Fernando; Maza, Iván; Merino, Luis; Viguria, Antidio; Ollero, Aníbal

2009-01-01

This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented.

A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis

NASA Astrophysics Data System (ADS)

Lu, Feng; Wang, Yafan; Huang, Jinquan; Wang, Qihang

2016-09-01

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

PubMed Central

Xia, Zhanguo; Xia, Shixiong; Wan, Ling; Cai, Shiyu

2012-01-01

Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017

Neural Network-Based Sensor Validation for Turboshaft Engines

NASA Technical Reports Server (NTRS)

Moller, James C.; Litt, Jonathan S.; Guo, Ten-Huei

1998-01-01

Sensor failure detection, isolation, and accommodation using a neural network approach is described. An auto-associative neural network is configured to perform dimensionality reduction on the sensor measurement vector and provide estimated sensor values. The sensor validation scheme is applied in a simulation of the T700 turboshaft engine in closed loop operation. Performance is evaluated based on the ability to detect faults correctly and maintain stable and responsive engine operation. The set of sensor outputs used for engine control forms the network input vector. Analytical redundancy is verified by training networks of successively smaller bottleneck layer sizes. Training data generation and strategy are discussed. The engine maintained stable behavior in the presence of sensor hard failures. With proper selection of fault determination thresholds, stability was maintained in the presence of sensor soft failures.

Advanced Ground Systems Maintenance Functional Fault Models For Fault Isolation Project

NASA Technical Reports Server (NTRS)

Perotti, Jose M. (Compiler)

2014-01-01

This project implements functional fault models (FFM) to automate the isolation of failures during ground systems operations. FFMs will also be used to recommend sensor placement to improve fault isolation capabilities. The project enables the delivery of system health advisories to ground system operators.

Model-Based Sensor Placement for Component Condition Monitoring and Fault Diagnosis in Fossil Energy Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mobed, Parham; Pednekar, Pratik; Bhattacharyya, Debangsu

Design and operation of energy producing, near “zero-emission” coal plants has become a national imperative. This report on model-based sensor placement describes a transformative two-tier approach to identify the optimum placement, number, and type of sensors for condition monitoring and fault diagnosis in fossil energy system operations. The algorithms are tested on a high fidelity model of the integrated gasification combined cycle (IGCC) plant. For a condition monitoring network, whether equipment should be considered at a unit level or a systems level depends upon the criticality of the process equipment, its likeliness to fail, and the level of resolution desiredmore » for any specific failure. Because of the presence of a high fidelity model at the unit level, a sensor network can be designed to monitor the spatial profile of the states and estimate fault severity levels. In an IGCC plant, besides the gasifier, the sour water gas shift (WGS) reactor plays an important role. In view of this, condition monitoring of the sour WGS reactor is considered at the unit level, while a detailed plant-wide model of gasification island, including sour WGS reactor and the Selexol process, is considered for fault diagnosis at the system-level. Finally, the developed algorithms unify the two levels and identifies an optimal sensor network that maximizes the effectiveness of the overall system-level fault diagnosis and component-level condition monitoring. This work could have a major impact on the design and operation of future fossil energy plants, particularly at the grassroots level where the sensor network is yet to be identified. In addition, the same algorithms developed in this report can be further enhanced to be used in retrofits, where the objectives could be upgrade (addition of more sensors) and relocation of existing sensors.« less

A distributed fault-detection and diagnosis system using on-line parameter estimation

NASA Technical Reports Server (NTRS)

Guo, T.-H.; Merrill, W.; Duyar, A.

1991-01-01

The development of a model-based fault-detection and diagnosis system (FDD) is reviewed. The system can be used as an integral part of an intelligent control system. It determines the faults of a system from comparison of the measurements of the system with a priori information represented by the model of the system. The method of modeling a complex system is described and a description of diagnosis models which include process faults is presented. There are three distinct classes of fault modes covered by the system performance model equation: actuator faults, sensor faults, and performance degradation. A system equation for a complete model that describes all three classes of faults is given. The strategy for detecting the fault and estimating the fault parameters using a distributed on-line parameter identification scheme is presented. A two-step approach is proposed. The first step is composed of a group of hypothesis testing modules, (HTM) in parallel processing to test each class of faults. The second step is the fault diagnosis module which checks all the information obtained from the HTM level, isolates the fault, and determines its magnitude. The proposed FDD system was demonstrated by applying it to detect actuator and sensor faults added to a simulation of the Space Shuttle Main Engine. The simulation results show that the proposed FDD system can adequately detect the faults and estimate their magnitudes.

Smart intimation and location of faults in distribution system

NASA Astrophysics Data System (ADS)

Hari Krishna, K.; Srinivasa Rao, B.

2018-04-01

Location of faults in the distribution system is one of the most complicated problems that we are facing today. Identification of fault location and severity of fault within a short time is required to provide continuous power supply but fault identification and information transfer to the operator is the biggest challenge in the distribution network. This paper proposes a fault location method in the distribution system based on Arduino nano and GSM module with flame sensor. The main idea is to locate the fault in the distribution transformer by sensing the arc coming out from the fuse element. The biggest challenge in the distribution network is to identify the location and the severity of faults under different conditions. Well operated transmission and distribution systems will play a key role for uninterrupted power supply. Whenever fault occurs in the distribution system the time taken to locate and eliminate the fault has to be reduced. The proposed design was achieved with flame sensor and GSM module. Under faulty condition, the system will automatically send an alert message to the operator in the distribution system, about the abnormal conditions near the transformer, site code and its exact location for possible power restoration.

Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

PubMed Central

Wang, Lijuan; Wu, Lifeng; Guan, Yong; Wang, Guohui

2015-01-01

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model. PMID:25690553

Analytical sensor redundancy assessment

NASA Technical Reports Server (NTRS)

Mulcare, D. B.; Downing, L. E.; Smith, M. K.

1988-01-01

The rationale and mechanization of sensor fault tolerance based on analytical redundancy principles are described. The concept involves the substitution of software procedures, such as an observer algorithm, to supplant additional hardware components. The observer synthesizes values of sensor states in lieu of their direct measurement. Such information can then be used, for example, to determine which of two disagreeing sensors is more correct, thus enhancing sensor fault survivability. Here a stability augmentation system is used as an example application, with required modifications being made to a quadruplex digital flight control system. The impact on software structure and the resultant revalidation effort are illustrated as well. Also, the use of an observer algorithm for wind gust filtering of the angle-of-attack sensor signal is presented.

Rolling bearing fault diagnosis based on information fusion using Dempster-Shafer evidence theory

NASA Astrophysics Data System (ADS)

Pei, Di; Yue, Jianhai; Jiao, Jing

2017-10-01

This paper presents a fault diagnosis method for rolling bearing based on information fusion. Acceleration sensors are arranged at different position to get bearing vibration data as diagnostic evidence. The Dempster-Shafer (D-S) evidence theory is used to fuse multi-sensor data to improve diagnostic accuracy. The efficiency of the proposed method is demonstrated by the high speed train transmission test bench. The results of experiment show that the proposed method in this paper improves the rolling bearing fault diagnosis accuracy compared with traditional signal analysis methods.

Sensor Data Qualification System (SDQS) Implementation Study

NASA Technical Reports Server (NTRS)

Wong, Edmond; Melcher, Kevin; Fulton, Christopher; Maul, William

2009-01-01

The Sensor Data Qualification System (SDQS) is being developed to provide a sensor fault detection capability for NASA s next-generation launch vehicles. In addition to traditional data qualification techniques (such as limit checks, rate-of-change checks and hardware redundancy checks), SDQS can provide augmented capability through additional techniques that exploit analytical redundancy relationships to enable faster and more sensitive sensor fault detection. This paper documents the results of a study that was conducted to determine the best approach for implementing a SDQS network configuration that spans multiple subsystems, similar to those that may be implemented on future vehicles. The best approach is defined as one that most minimizes computational resource requirements without impacting the detection of sensor failures.

Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network

NASA Astrophysics Data System (ADS)

Lu, Siliang; Zhou, Peng; Wang, Xiaoxian; Liu, Yongbin; Liu, Fang; Zhao, Jiwen

2018-02-01

Wireless sensor networks (WSNs) which consist of miscellaneous sensors are used frequently in monitoring vital equipment. Benefiting from the development of data mining technologies, the massive data generated by sensors facilitate condition monitoring and fault diagnosis. However, too much data increase storage space, energy consumption, and computing resource, which can be considered fatal weaknesses for a WSN with limited resources. This study investigates a new method for motor bearings condition monitoring and fault diagnosis using the undersampled vibration signals acquired from a WSN. The proposed method, which is a fusion of the kurtogram, analog domain bandpass filtering, bandpass sampling, and demodulated resonance technique, can reduce the sampled data length while retaining the monitoring and diagnosis performance. A WSN prototype was designed, and simulations and experiments were conducted to evaluate the effectiveness and efficiency of the proposed method. Experimental results indicated that the sampled data length and transmission time of the proposed method result in a decrease of over 80% in comparison with that of the traditional method. Therefore, the proposed method indicates potential applications on condition monitoring and fault diagnosis of motor bearings installed in remote areas, such as wind farms and offshore platforms.

Sensor fault detection and isolation via high-gain observers: application to a double-pipe heat exchanger.

PubMed

Escobar, R F; Astorga-Zaragoza, C M; Téllez-Anguiano, A C; Juárez-Romero, D; Hernández, J A; Guerrero-Ramírez, G V

2011-07-01

This paper deals with fault detection and isolation (FDI) in sensors applied to a concentric-pipe counter-flow heat exchanger. The proposed FDI is based on the analytical redundancy implementing nonlinear high-gain observers which are used to generate residuals when a sensor fault is presented (as software sensors). By evaluating the generated residual, it is possible to switch between the sensor and the observer when a failure is detected. Experiments in a heat exchanger pilot validate the effectiveness of the approach. The FDI technique is easy to implement allowing the industries to have an excellent alternative tool to keep their heat transfer process under supervision. The main contribution of this work is based on a dynamic model with heat transfer coefficients which depend on temperature and flow used to estimate the output temperatures of a heat exchanger. This model provides a satisfactory approximation of the states of the heat exchanger in order to allow its implementation in a FDI system used to perform supervision tasks. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive neural network/expert system that learns fault diagnosis for different structures

NASA Astrophysics Data System (ADS)

Simon, Solomon H.

1992-08-01

Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

The Hayward Fault Exposed! 20,000 Visitors Made it a Success

NASA Astrophysics Data System (ADS)

Stenner, H.; Zoback, M.; Schwartz, D.

2007-12-01

Last year, as part of the commemoration of the anniversary of the 1906 earthquake, an exhibit was built that gave the public a chance to better understand earthquakes and the faults that create them, and how to be prepared for a major earthquake. Open for six months, the exhibit in Fremont Central Park attracted more than 20,000 visitors from throughout the San Francisco Bay area and beyond. The main draw was the opportunity to descend into a 12-foot-deep excavation that provided up-close views of the Hayward fault itself. Visitors came to see the fault but stayed to hear its story and view displays about being prepared for the coming quake and the science behind it. The Hayward fault is an excellent subject to spark public interest. The large 1868 earthquake, which was known as "the great San Francisco earthquake" until 1906, caused the Hayward fault to slip up to 6 feet in areas that are now densely urbanized with homes and town centers. Further, the fault has been researched extensively, revealing that we are currently in the time window during which the next big earthquake, perhaps a repeat of the 1868 earthquake, is likely to occur along the Hayward fault. And to top it off, the fault experiences tectonic creep that provides fairly dramatic evidence of fault movement by cracking and offsetting curbs, parking lots, and streets near the exhibit site. Visitor feedback was overwhelmingly positive. Local groups came en masse and were spurred into developing plans for responding to a large earthquake in their community. School children came on field trips, saw what a fault looks like and how fault movement affects what they think of as static features of their world. Many visitors mentioned that such an exhibit should be a permanent Bay Area attraction. Two years in planning, the event required large amounts of volunteer time, sponsorship funds, agreement from the local government, and dedication from its developers. A permanent exhibit would undoubtedly be successful. It is the funding and support of the local government that are the biggest challenges. Now that the idea of an Earthquake and Fault Exhibit has proven successful, the common pre-exhibit question of: "Who would want to see a big hole in the ground?" is easy to answer.

Onboard Sensor Data Qualification in Human-Rated Launch Vehicles

NASA Technical Reports Server (NTRS)

Wong, Edmond; Melcher, Kevin J.; Maul, William A.; Chicatelli, Amy K.; Sowers, Thomas S.; Fulton, Christopher; Bickford, Randall

2012-01-01

The avionics system software for human-rated launch vehicles requires an implementation approach that is robust to failures, especially the failure of sensors used to monitor vehicle conditions that might result in an abort determination. Sensor measurements provide the basis for operational decisions on human-rated launch vehicles. This data is often used to assess the health of system or subsystem components, to identify failures, and to take corrective action. An incorrect conclusion and/or response may result if the sensor itself provides faulty data, or if the data provided by the sensor has been corrupted. Operational decisions based on faulty sensor data have the potential to be catastrophic, resulting in loss of mission or loss of crew. To prevent these later situations from occurring, a Modular Architecture and Generalized Methodology for Sensor Data Qualification in Human-rated Launch Vehicles has been developed. Sensor Data Qualification (SDQ) is a set of algorithms that can be implemented in onboard flight software, and can be used to qualify data obtained from flight-critical sensors prior to the data being used by other flight software algorithms. Qualified data has been analyzed by SDQ and is determined to be a true representation of the sensed system state; that is, the sensor data is determined not to be corrupted by sensor faults or signal transmission faults. Sensor data can become corrupted by faults at any point in the signal path between the sensor and the flight computer. Qualifying the sensor data has the benefit of ensuring that erroneous data is identified and flagged before otherwise being used for operational decisions, thus increasing confidence in the response of the other flight software processes using the qualified data, and decreasing the probability of false alarms or missed detections.

Adaptively Adjusted Event-Triggering Mechanism on Fault Detection for Networked Control Systems.

PubMed

Wang, Yu-Long; Lim, Cheng-Chew; Shi, Peng

2016-12-08

This paper studies the problem of adaptively adjusted event-triggering mechanism-based fault detection for a class of discrete-time networked control system (NCS) with applications to aircraft dynamics. By taking into account the fault occurrence detection progress and the fault occurrence probability, and introducing an adaptively adjusted event-triggering parameter, a novel event-triggering mechanism is proposed to achieve the efficient utilization of the communication network bandwidth. Both the sensor-to-control station and the control station-to-actuator network-induced delays are taken into account. The event-triggered sensor and the event-triggered control station are utilized simultaneously to establish new network-based closed-loop models for the NCS subject to faults. Based on the established models, the event-triggered simultaneous design of fault detection filter (FDF) and controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. Performance analysis verifies the effectiveness of the adaptively adjusted event-triggering mechanism, and the simultaneous design of FDF and controller.

Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

NASA Astrophysics Data System (ADS)

Jiao, Jing; Yue, Jianhai; Pei, Di

2017-10-01

Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.

75 FR 71371 - Airworthiness Directives; Thielert Aircraft Engines GmbH Models TAE 125-01, TAE 125-02-99, and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-23

... permeability is not always recognized as fault by the FADEC. The MAP value measured by the sensor may be lower... channel B manifold air pressure (MAP) sensor hose permeability is not always recognized as fault by the... between the national Government and the States, or on the distribution of power and responsibilities among...

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter.

PubMed

Yang, Chun; Mohammadi, Arash; Chen, Qing-Wei

2016-11-02

Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system's error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the proposed fault tolerant fusion framework provides superior performance over its traditional counterparts.

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter

PubMed Central

Yang, Chun; Mohammadi, Arash; Chen, Qing-Wei

2016-01-01

Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system’s error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the proposed fault tolerant fusion framework provides superior performance over its traditional counterparts. PMID:27827832

A Virtual Sensor for Online Fault Detection of Multitooth-Tools

PubMed Central

Bustillo, Andres; Correa, Maritza; Reñones, Anibal

2011-01-01

The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases. PMID:22163766

A virtual sensor for online fault detection of multitooth-tools.

PubMed

Bustillo, Andres; Correa, Maritza; Reñones, Anibal

2011-01-01

The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases.

An Integrated FDD System for HVAC&R Based on Virtual Sensors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Woohyun

According to the U.S Department of Energy, space heating, ventilation and air conditioning system account for 40% of residential primary energy use and for 30% of primary energy use in commercial buildings. A study released by the Energy Information Administration indicated that packaged air conditioners are widely used in 46% of all commercial buildings in the U.S. This study indicates that the annual cooling energy consumption related to the packaged air conditioner is about 160 trillion Btus. Therefore, an automated FDD system that can automatically detect and diagnose faults and evaluate fault impacts has the potential for improving energy efficiencymore » along with reducing service costs and comfort complaints. The primary bottlenecks to diagnostic implementation in the field are the high initial costs of additional sensors. To prevent those limitations, virtual sensors with low cost measurements and simple models are developed to estimate quantities that would be expensive and or difficult to measure directly. The use of virtual sensors can reduce costs compared to the use of real sensors and provide additional information for economic assessment. The virtual sensor can be embedded in a permanently installed control or monitoring system and continuous monitoring potentially leads to early detection of faults. The virtual sensors of individual equipment components can be integrated to estimate overall diagnostic information using the output of each virtual sensor.« less

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis.

PubMed

Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang; Hu, Jianjun

2017-07-28

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

PubMed Central

Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang

2017-01-01

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions. PMID:28788099

Simplified Interval Observer Scheme: A New Approach for Fault Diagnosis in Instruments

PubMed Central

Martínez-Sibaja, Albino; Astorga-Zaragoza, Carlos M.; Alvarado-Lassman, Alejandro; Posada-Gómez, Rubén; Aguila-Rodríguez, Gerardo; Rodríguez-Jarquin, José P.; Adam-Medina, Manuel

2011-01-01

There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of fault diagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors. PMID:22346593

Improved chemical identification from sensor arrays using intelligent algorithms

NASA Astrophysics Data System (ADS)

Roppel, Thaddeus A.; Wilson, Denise M.

2001-02-01

Intelligent signal processing algorithms are shown to improve identification rates significantly in chemical sensor arrays. This paper focuses on the use of independently derived sensor status information to modify the processing of sensor array data by using a fast, easily-implemented "best-match" approach to filling in missing sensor data. Most fault conditions of interest (e.g., stuck high, stuck low, sudden jumps, excess noise, etc.) can be detected relatively simply by adjunct data processing, or by on-board circuitry. The objective then is to devise, implement, and test methods for using this information to improve the identification rates in the presence of faulted sensors. In one typical example studied, utilizing separately derived, a-priori knowledge about the health of the sensors in the array improved the chemical identification rate by an artificial neural network from below 10 percent correct to over 99 percent correct. While this study focuses experimentally on chemical sensor arrays, the results are readily extensible to other types of sensor platforms.

Identification of faulty sensor using relative partial decomposition via independent component analysis

NASA Astrophysics Data System (ADS)

Wang, Z.; Quek, S. T.

2015-07-01

Performance of any structural health monitoring algorithm relies heavily on good measurement data. Hence, it is necessary to employ robust faulty sensor detection approaches to isolate sensors with abnormal behaviour and exclude the highly inaccurate data in the subsequent analysis. The independent component analysis (ICA) is implemented to detect the presence of sensors showing abnormal behaviour. A normalized form of the relative partial decomposition contribution (rPDC) is proposed to identify the faulty sensor. Both additive and multiplicative types of faults are addressed and the detectability illustrated using a numerical and an experimental example. An empirical method to establish control limits for detecting and identifying the type of fault is also proposed. The results show the effectiveness of the ICA and rPDC method in identifying faulty sensor assuming that baseline cases are available.

Sensor fault detection and isolation system for a condensation process.

PubMed

Castro, M A López; Escobar, R F; Torres, L; Aguilar, J F Gómez; Hernández, J A; Olivares-Peregrino, V H

2016-11-01

This article presents the design of a sensor Fault Detection and Isolation (FDI) system for a condensation process based on a nonlinear model. The condenser is modeled by dynamic and thermodynamic equations. For this work, the dynamic equations are described by three pairs of differential equations which represent the energy balance between the fluids. The thermodynamic equations consist in algebraic heat transfer equations and empirical equations, that allow for the estimation of heat transfer coefficients. The FDI system consists of a bank of two nonlinear high-gain observers, in order to detect, estimate and to isolate the fault in any of both outlet temperature sensors. The main contributions of this work were the experimental validation of the condenser nonlinear model and the FDI system. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

PubMed Central

Moghadas, Amin A.; Shadaram, Mehdi

2010-01-01

In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG). The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system. PMID:22163416

Real-Time Fault Classification for Plasma Processes

PubMed Central

Yang, Ryan; Chen, Rongshun

2011-01-01

Plasma process tools, which usually cost several millions of US dollars, are often used in the semiconductor fabrication etching process. If the plasma process is halted due to some process fault, the productivity will be reduced and the cost will increase. In order to maximize the product/wafer yield and tool productivity, a timely and effective fault process detection is required in a plasma reactor. The classification of fault events can help the users to quickly identify fault processes, and thus can save downtime of the plasma tool. In this work, optical emission spectroscopy (OES) is employed as the metrology sensor for in-situ process monitoring. Splitting into twelve different match rates by spectrum bands, the matching rate indicator in our previous work (Yang, R.; Chen, R.S. Sensors 2010, 10, 5703–5723) is used to detect the fault process. Based on the match data, a real-time classification of plasma faults is achieved by a novel method, developed in this study. Experiments were conducted to validate the novel fault classification. From the experimental results, we may conclude that the proposed method is feasible inasmuch that the overall accuracy rate of the classification for fault event shifts is 27 out of 28 or about 96.4% in success. PMID:22164001

Flight test results of the Strapdown hexad Inertial Reference Unit (SIRU). Volume 1: Flight test summary

NASA Technical Reports Server (NTRS)

Hruby, R. J.; Bjorkman, W. S.

1977-01-01

Flight test results of the strapdown inertial reference unit (SIRU) navigation system are presented. The fault-tolerant SIRU navigation system features a redundant inertial sensor unit and dual computers. System software provides for detection and isolation of inertial sensor failures and continued operation in the event of failures. Flight test results include assessments of the system's navigational performance and fault tolerance.

QuakeSim: a Web Service Environment for Productive Investigations with Earth Surface Sensor Data

NASA Astrophysics Data System (ADS)

Parker, J. W.; Donnellan, A.; Granat, R. A.; Lyzenga, G. A.; Glasscoe, M. T.; McLeod, D.; Al-Ghanmi, R.; Pierce, M.; Fox, G.; Grant Ludwig, L.; Rundle, J. B.

2011-12-01

The QuakeSim science gateway environment includes a visually rich portal interface, web service access to data and data processing operations, and the QuakeTables ontology-based database of fault models and sensor data. The integrated tools and services are designed to assist investigators by covering the entire earthquake cycle of strain accumulation and release. The Web interface now includes Drupal-based access to diverse and changing content, with new ability to access data and data processing directly from the public page, as well as the traditional project management areas that require password access. The system is designed to make initial browsing of fault models and deformation data particularly engaging for new users. Popular data and data processing include GPS time series with data mining techniques to find anomalies in time and space, experimental forecasting methods based on catalogue seismicity, faulted deformation models (both half-space and finite element), and model-based inversion of sensor data. The fault models include the CGS and UCERF 2.0 faults of California and are easily augmented with self-consistent fault models from other regions. The QuakeTables deformation data include the comprehensive set of UAVSAR interferograms as well as a growing collection of satellite InSAR data.. Fault interaction simulations are also being incorporated in the web environment based on Virtual California. A sample usage scenario is presented which follows an investigation of UAVSAR data from viewing as an overlay in Google Maps, to selection of an area of interest via a polygon tool, to fast extraction of the relevant correlation and phase information from large data files, to a model inversion of fault slip followed by calculation and display of a synthetic model interferogram.

Machine learning of fault characteristics from rocket engine simulation data

NASA Technical Reports Server (NTRS)

Ke, Min; Ali, Moonis

1990-01-01

Transformation of data into knowledge through conceptual induction has been the focus of our research described in this paper. We have developed a Machine Learning System (MLS) to analyze the rocket engine simulation data. MLS can provide to its users fault analysis, characteristics, and conceptual descriptions of faults, and the relationships of attributes and sensors. All the results are critically important in identifying faults.

A Novel Transient Fault Current Sensor Based on the PCB Rogowski Coil for Overhead Transmission Lines

PubMed Central

Liu, Yadong; Xie, Xiaolei; Hu, Yue; Qian, Yong; Sheng, Gehao; Jiang, Xiuchen

2016-01-01

The accurate detection of high-frequency transient fault currents in overhead transmission lines is the basis of malfunction detection and diagnosis. This paper proposes a novel differential winding printed circuit board (PCB) Rogowski coil for the detection of transient fault currents in overhead transmission lines. The interference mechanism of the sensor surrounding the overhead transmission line is analyzed and the guideline for the interference elimination is obtained, and then a differential winding printed circuit board (PCB) Rogowski coil is proposed, where the branch and return line of the PCB coil were designed to be strictly symmetrical by using a joining structure of two semi-rings and collinear twisted pair differential windings in each semi-ring. A serial test is conducted, including the frequency response, linearity, and anti-interference performance as well as a comparison with commercial sensors. Results show that a PCB Rogowski coil has good linearity and resistance to various external magnetic field interferences, thus enabling it to be widely applied in fault-current-collecting devices. PMID:27213402

A flight expert system (FLES) for on-board fault monitoring and diagnosis

NASA Technical Reports Server (NTRS)

Ali, Moonis; Scharnhorst, D. A.; Ai, C. S.; Feber, H. J.

1987-01-01

The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher workloads for the pilot and tend to induce a higher probability of errors. Therefore, an architecture for a flight expert system (FLES) is developed to assist pilots in monitoring, diagnosing and recovering from in-flight faults. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with airplane status information during the diagnostic process. The simulator is based on the Lockheed Advanced Concept System (ACS), a future generation airplane, and on the Boeing 737. A distinction between two types of faults, maladjustments and malfunctions, has led to two approaches to fault diagnosis. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to access abnormal situations during airplane flight. Malfunctions and maladjustments are handled separately, diagnosed using domain knowledge.

Proprioceptive Sensors' Fault Tolerant Control Strategy for an Autonomous Vehicle.

PubMed

Boukhari, Mohamed Riad; Chaibet, Ahmed; Boukhnifer, Moussa; Glaser, Sébastien

2018-06-09

In this contribution, a fault-tolerant control strategy for the longitudinal dynamics of an autonomous vehicle is presented. The aim is to be able to detect potential failures of the vehicle's speed sensor and then to keep the vehicle in a safe state. For this purpose, the separation principle, composed of a static output feedback controller and fault estimation observers, is designed. Indeed, two observer techniques were proposed: the proportional and integral observer and the descriptor observer. The effectiveness of the proposed scheme is validated by means of the experimental demonstrator of the VEDECOM (Véhicle Décarboné et Communinicant) Institut.

A Fault Tolerance Mechanism for On-Road Sensor Networks

PubMed Central

Feng, Lei; Guo, Shaoyong; Sun, Jialu; Yu, Peng; Li, Wenjing

2016-01-01

On-Road Sensor Networks (ORSNs) play an important role in capturing traffic flow data for predicting short-term traffic patterns, driving assistance and self-driving vehicles. However, this kind of network is prone to large-scale communication failure if a few sensors physically fail. In this paper, to ensure that the network works normally, an effective fault-tolerance mechanism for ORSNs which mainly consists of backup on-road sensor deployment, redundant cluster head deployment and an adaptive failure detection and recovery method is proposed. Firstly, based on the N − x principle and the sensors’ failure rate, this paper formulates the backup sensor deployment problem in the form of a two-objective optimization, which explains the trade-off between the cost and fault resumption. In consideration of improving the network resilience further, this paper introduces a redundant cluster head deployment model according to the coverage constraint. Then a common solving method combining integer-continuing and sequential quadratic programming is explored to determine the optimal location of these two deployment problems. Moreover, an Adaptive Detection and Resume (ADR) protocol is deigned to recover the system communication through route and cluster adjustment if there is a backup on-road sensor mismatch. The final experiments show that our proposed mechanism can achieve an average 90% recovery rate and reduce the average number of failed sensors at most by 35.7%. PMID:27918483

Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank of Kalman Filters

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L. (Technical Monitor)

2003-01-01

In this report, a fault detection and isolation (FDI) system which utilizes a bank of Kalman filters is developed for aircraft engine sensor and actuator FDI in conjunction with the detection of component faults. This FDI approach uses multiple Kalman filters, each of which is designed based on a specific hypothesis for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, from which a specific fault is isolated. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The performance of the FDI system is evaluated against a nonlinear engine simulation for various engine faults at cruise operating conditions. In order to mimic the real engine environment, the nonlinear simulation is executed not only at the nominal, or healthy, condition but also at aged conditions. When the FDI system designed at the healthy condition is applied to an aged engine, the effectiveness of the FDI system is impacted by the mismatch in the engine health condition. Depending on its severity, this mismatch can cause the FDI system to generate incorrect diagnostic results, such as false alarms and missed detections. To partially recover the nominal performance, two approaches, which incorporate information regarding the engine s aging condition in the FDI system, will be discussed and evaluated. The results indicate that the proposed FDI system is promising for reliable diagnostics of aircraft engines.

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

PubMed Central

Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

2017-01-01

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

Health management and controls for Earth-to-orbit propulsion systems

NASA Astrophysics Data System (ADS)

Bickford, R. L.

1995-03-01

Avionics and health management technologies increase the safety and reliability while decreasing the overall cost for Earth-to-orbit (ETO) propulsion systems. New ETO propulsion systems will depend on highly reliable fault tolerant flight avionics, advanced sensing systems and artificial intelligence aided software to ensure critical control, safety and maintenance requirements are met in a cost effective manner. Propulsion avionics consist of the engine controller, actuators, sensors, software and ground support elements. In addition to control and safety functions, these elements perform system monitoring for health management. Health management is enhanced by advanced sensing systems and algorithms which provide automated fault detection and enable adaptive control and/or maintenance approaches. Aerojet is developing advanced fault tolerant rocket engine controllers which provide very high levels of reliability. Smart sensors and software systems which significantly enhance fault coverage and enable automated operations are also under development. Smart sensing systems, such as flight capable plume spectrometers, have reached maturity in ground-based applications and are suitable for bridging to flight. Software to detect failed sensors has reached similar maturity. This paper will discuss fault detection and isolation for advanced rocket engine controllers as well as examples of advanced sensing systems and software which significantly improve component failure detection for engine system safety and health management.

An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis

PubMed Central

Zhou, Deyun; Zhuang, Miaoyan; Fang, Xueyi; Xie, Chunhe

2017-01-01

As an important tool of information fusion, Dempster–Shafer evidence theory is widely applied in handling the uncertain information in fault diagnosis. However, an incorrect result may be obtained if the combined evidence is highly conflicting, which may leads to failure in locating the fault. To deal with the problem, an improved evidential-Induced Ordered Weighted Averaging (IOWA) sensor data fusion approach is proposed in the frame of Dempster–Shafer evidence theory. In the new method, the IOWA operator is used to determine the weight of different sensor data source, while determining the parameter of the IOWA, both the distance of evidence and the belief entropy are taken into consideration. First, based on the global distance of evidence and the global belief entropy, the α value of IOWA is obtained. Simultaneously, a weight vector is given based on the maximum entropy method model. Then, according to IOWA operator, the evidence are modified before applying the Dempster’s combination rule. The proposed method has a better performance in conflict management and fault diagnosis due to the fact that the information volume of each evidence is taken into consideration. A numerical example and a case study in fault diagnosis are presented to show the rationality and efficiency of the proposed method. PMID:28927017

Ultrathin Au-Alloy Nanowires at the Liquid-Liquid Interface.

PubMed

Chatterjee, Dipanwita; Shetty, Shwetha; Müller-Caspary, Knut; Grieb, Tim; Krause, Florian F; Schowalter, Marco; Rosenauer, Andreas; Ravishankar, Narayanan

2018-03-14

Ultrathin bimetallic nanowires are of importance and interest for applications in electronic devices such as sensors and heterogeneous catalysts. In this work, we have designed a new, highly reproducible and generalized wet chemical method to synthesize uniform and monodispersed Au-based alloy (AuCu, AuPd, and AuPt) nanowires with tunable composition using microwave-assisted reduction at the liquid-liquid interface. These ultrathin alloy nanowires are below 4 nm in diameter and about 2 μm long. Detailed microstructural characterization shows that the wires have an face centred cubic (FCC) crystal structure, and they have low-energy twin-boundary and stacking-fault defects along the growth direction. The wires exhibit remarkable thermal and mechanical stability that is critical for important applications. The alloy wires exhibit excellent electrocatalytic activity for methanol oxidation in an alkaline medium.

Flight test results of the strapdown hexad inertial reference unit (SIRU). Volume 2: Test report

NASA Technical Reports Server (NTRS)

Hruby, R. J.; Bjorkman, W. S.

1977-01-01

Results of flight tests of the Strapdown Inertial Reference Unit (SIRU) navigation system are presented. The fault tolerant SIRU navigation system features a redundant inertial sensor unit and dual computers. System software provides for detection and isolation of inertial sensor failures and continued operation in the event of failures. Flight test results include assessments of the system's navigational performance and fault tolerance. Performance shortcomings are analyzed.

Gear-box fault detection using time-frequency based methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Odgaard, Peter Fogh; Stoustrup, Jakob

2015-01-01

Gear-box fault monitoring and detection is important for optimization of power generation and availability of wind turbines. The current industrial approach is to use condition monitoring systems, which runs in parallel with the wind turbine control system, using expensive additional sensors. An alternative would be to use the existing measurements which are normally available for the wind turbine control system. The usage of these sensors instead would cut down the cost of the wind turbine by not using additional sensors. One of these available measurements is the generator speed, in which changes in the gear-box resonance frequency can be detected.more » Two different time-frequency based approaches are presented in this paper. One is a filter based approach and the other is based on a Karhunen-Loeve basis. Both of them detects the gear-box fault with an acceptable detection delay.« less

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2002-01-01

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

Auto-Calibration and Fault Detection and Isolation of Skewed Redundant Accelerometers in Measurement While Drilling Systems.

PubMed

Seyed Moosavi, Seyed Mohsen; Moaveni, Bijan; Moshiri, Behzad; Arvan, Mohammad Reza

2018-02-27

The present study designed skewed redundant accelerometers for a Measurement While Drilling (MWD) tool and executed auto-calibration, fault diagnosis and isolation of accelerometers in this tool. The optimal structure includes four accelerometers was selected and designed precisely in accordance with the physical shape of the existing MWD tool. A new four-accelerometer structure was designed, implemented and installed on the current system, replacing the conventional orthogonal structure. Auto-calibration operation of skewed redundant accelerometers and all combinations of three accelerometers have been done. Consequently, biases, scale factors, and misalignment factors of accelerometers have been successfully estimated. By defecting the sensors in the new optimal skewed redundant structure, the fault was detected using the proposed FDI method and the faulty sensor was diagnosed and isolated. The results indicate that the system can continue to operate with at least three correct sensors.

Auto-Calibration and Fault Detection and Isolation of Skewed Redundant Accelerometers in Measurement While Drilling Systems

PubMed Central

Seyed Moosavi, Seyed Mohsen; Moshiri, Behzad; Arvan, Mohammad Reza

2018-01-01

The present study designed skewed redundant accelerometers for a Measurement While Drilling (MWD) tool and executed auto-calibration, fault diagnosis and isolation of accelerometers in this tool. The optimal structure includes four accelerometers was selected and designed precisely in accordance with the physical shape of the existing MWD tool. A new four-accelerometer structure was designed, implemented and installed on the current system, replacing the conventional orthogonal structure. Auto-calibration operation of skewed redundant accelerometers and all combinations of three accelerometers have been done. Consequently, biases, scale factors, and misalignment factors of accelerometers have been successfully estimated. By defecting the sensors in the new optimal skewed redundant structure, the fault was detected using the proposed FDI method and the faulty sensor was diagnosed and isolated. The results indicate that the system can continue to operate with at least three correct sensors. PMID:29495434

Robust fault detection of wind energy conversion systems based on dynamic neural networks.

PubMed

Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

2014-01-01

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

PubMed Central

Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

2014-01-01

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate. PMID:24744774

Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

NASA Astrophysics Data System (ADS)

Polverino, Pierpaolo; Frisk, Erik; Jung, Daniel; Krysander, Mattias; Pianese, Cesare

2017-07-01

The present paper proposes an advanced approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and isolation through a model-based diagnostic algorithm. The considered algorithm is developed upon a lumped parameter model simulating a whole PEMFC system oriented towards automotive applications. This model is inspired by other models available in the literature, with further attention to stack thermal dynamics and water management. The developed model is analysed by means of Structural Analysis, to identify the correlations among involved physical variables, defined equations and a set of faults which may occur in the system (related to both auxiliary components malfunctions and stack degradation phenomena). Residual generators are designed by means of Causal Computation analysis and the maximum theoretical fault isolability, achievable with a minimal number of installed sensors, is investigated. The achieved results proved the capability of the algorithm to theoretically detect and isolate almost all faults with the only use of stack voltage and temperature sensors, with significant advantages from an industrial point of view. The effective fault isolability is proved through fault simulations at a specific fault magnitude with an advanced residual evaluation technique, to consider quantitative residual deviations from normal conditions and achieve univocal fault isolation.

Discrete Data Qualification System and Method Comprising Noise Series Fault Detection

NASA Technical Reports Server (NTRS)

Fulton, Christopher; Wong, Edmond; Melcher, Kevin; Bickford, Randall

2013-01-01

A Sensor Data Qualification (SDQ) function has been developed that allows the onboard flight computers on NASA s launch vehicles to determine the validity of sensor data to ensure that critical safety and operational decisions are not based on faulty sensor data. This SDQ function includes a novel noise series fault detection algorithm for qualification of the output data from LO2 and LH2 low-level liquid sensors. These sensors are positioned in a launch vehicle s propellant tanks in order to detect propellant depletion during a rocket engine s boost operating phase. This detection capability can prevent the catastrophic situation where the engine operates without propellant. The output from each LO2 and LH2 low-level liquid sensor is a discrete valued signal that is expected to be in either of two states, depending on whether the sensor is immersed (wet) or exposed (dry). Conventional methods for sensor data qualification, such as threshold limit checking, are not effective for this type of signal due to its discrete binary-state nature. To address this data qualification challenge, a noise computation and evaluation method, also known as a noise fault detector, was developed to detect unreasonable statistical characteristics in the discrete data stream. The method operates on a time series of discrete data observations over a moving window of data points and performs a continuous examination of the resulting observation stream to identify the presence of anomalous characteristics. If the method determines the existence of anomalous results, the data from the sensor is disqualified for use by other monitoring or control functions.

Study on fault diagnosis and load feedback control system of combine harvester

NASA Astrophysics Data System (ADS)

Li, Ying; Wang, Kun

2017-01-01

In order to timely gain working status parameters of operating parts in combine harvester and improve its operating efficiency, fault diagnosis and load feedback control system is designed. In the system, rotation speed sensors were used to gather these signals of forward speed and rotation speeds of intermediate shaft, conveying trough, tangential and longitudinal flow threshing rotors, grain conveying auger. Using C8051 single chip microcomputer (SCM) as processor for main control unit, faults diagnosis and forward speed control were carried through by rotation speed ratio analysis of each channel rotation speed and intermediate shaft rotation speed by use of multi-sensor fused fuzzy control algorithm, and these processing results would be sent to touch screen and display work status of combine harvester. Field trials manifest that fault monitoring and load feedback control system has good man-machine interaction and the fault diagnosis method based on rotation speed ratios has low false alarm rate, and the system can realize automation control of forward speed for combine harvester.

Active fault tolerant control based on interval type-2 fuzzy sliding mode controller and non linear adaptive observer for 3-DOF laboratory helicopter.

PubMed

Zeghlache, Samir; Benslimane, Tarak; Bouguerra, Abderrahmen

2017-11-01

In this paper, a robust controller for a three degree of freedom (3 DOF) helicopter control is proposed in presence of actuator and sensor faults. For this purpose, Interval type-2 fuzzy logic control approach (IT2FLC) and sliding mode control (SMC) technique are used to design a controller, named active fault tolerant interval type-2 Fuzzy Sliding mode controller (AFTIT2FSMC) based on non-linear adaptive observer to estimate and detect the system faults for each subsystem of the 3-DOF helicopter. The proposed control scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller. Exponential stability of the closed loop is guaranteed by using the Lyapunov method. The simulation results show that the AFTIT2FSMC can greatly alleviate the chattering effect, providing good tracking performance, even in presence of actuator and sensor faults. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

The Design and Semi-Physical Simulation Test of Fault-Tolerant Controller for Aero Engine

NASA Astrophysics Data System (ADS)

Liu, Yuan; Zhang, Xin; Zhang, Tianhong

2017-11-01

A new fault-tolerant control method for aero engine is proposed, which can accurately diagnose the sensor fault by Kalman filter banks and reconstruct the signal by real-time on-board adaptive model combing with a simplified real-time model and an improved Kalman filter. In order to verify the feasibility of the method proposed, a semi-physical simulation experiment has been carried out. Besides the real I/O interfaces, controller hardware and the virtual plant model, semi-physical simulation system also contains real fuel system. Compared with the hardware-in-the-loop (HIL) simulation, semi-physical simulation system has a higher degree of confidence. In order to meet the needs of semi-physical simulation, a rapid prototyping controller with fault-tolerant control ability based on NI CompactRIO platform is designed and verified on the semi-physical simulation test platform. The result shows that the controller can realize the aero engine control safely and reliably with little influence on controller performance in the event of fault on sensor.

Real-Time Diagnosis of Faults Using a Bank of Kalman Filters

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2006-01-01

A new robust method of automated real-time diagnosis of faults in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time fault diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient faults while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator fault. When a sensor or actuator fault occurs, large estimation errors are generated by all filters except the one using the correct hypothesis. By monitoring the residual output of each filter, the specific fault that has occurred can be detected and isolated on the basis of the decision rules. A set of parameters that indicate the performance of the engine components is estimated by the "correct" Kalman filter for use in detecting component faults. To reduce the loss of diagnostic accuracy and sensitivity in the face of aging, the FDI system accepts information from a steady-state-condition-monitoring system. This information is used to update the Kalman filters and a data bank of trim values representative of the current aging condition.

Evaluation of a fault tolerant system for an integrated avionics sensor configuration with TSRV flight data

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Godiwala, P. M.

1985-01-01

The performance analysis results of a fault inferring nonlinear detection system (FINDS) using sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment is presented. First, a statistical analysis of the flight recorded sensor data was made in order to determine the characteristics of sensor inaccuracies. Next, modifications were made to the detection and decision functions in the FINDS algorithm in order to improve false alarm and failure detection performance under real modelling errors present in the flight data. Finally, the failure detection and false alarm performance of the FINDS algorithm were analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minute flight data. In general, the detection speed, failure level estimation, and false alarm performance showed a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed was faster for filter measurement sensors soon as MLS than for filter input sensors such as flight control accelerometers.

Fault Tolerant Airborne Sensor Networks for Air Operations

DTIC Science & Technology

2008-02-01

lives affected by undetected targets. The network is said to have expired when there is no longer a single surviving sensor-pair. Tasking process...tasking a finite number of cooperative agents to randomly emerging targets for their removal. Faults occur when some agents engaged in a mission are...expired. Agents are subject to threat at a level determined by the number of targets present. On the other hand, the rate at which a target is removed

Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine

NASA Technical Reports Server (NTRS)

Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.

1992-01-01

In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

Robot Position Sensor Fault Tolerance

NASA Technical Reports Server (NTRS)

Aldridge, Hal A.

1997-01-01

Robot systems in critical applications, such as those in space and nuclear environments, must be able to operate during component failure to complete important tasks. One failure mode that has received little attention is the failure of joint position sensors. Current fault tolerant designs require the addition of directly redundant position sensors which can affect joint design. A new method is proposed that utilizes analytical redundancy to allow for continued operation during joint position sensor failure. Joint torque sensors are used with a virtual passive torque controller to make the robot joint stable without position feedback and improve position tracking performance in the presence of unknown link dynamics and end-effector loading. Two Cartesian accelerometer based methods are proposed to determine the position of the joint. The joint specific position determination method utilizes two triaxial accelerometers attached to the link driven by the joint with the failed position sensor. The joint specific method is not computationally complex and the position error is bounded. The system wide position determination method utilizes accelerometers distributed on different robot links and the end-effector to determine the position of sets of multiple joints. The system wide method requires fewer accelerometers than the joint specific method to make all joint position sensors fault tolerant but is more computationally complex and has lower convergence properties. Experiments were conducted on a laboratory manipulator. Both position determination methods were shown to track the actual position satisfactorily. A controller using the position determination methods and the virtual passive torque controller was able to servo the joints to a desired position during position sensor failure.

Adaptive and technology-independent architecture for fault-tolerant distributed AAL solutions.

PubMed

Schmidt, Michael; Obermaisser, Roman

2018-04-01

Today's architectures for Ambient Assisted Living (AAL) must cope with a variety of challenges like flawless sensor integration and time synchronization (e.g. for sensor data fusion) while abstracting from the underlying technologies at the same time. Furthermore, an architecture for AAL must be capable to manage distributed application scenarios in order to support elderly people in all situations of their everyday life. This encompasses not just life at home but in particular the mobility of elderly people (e.g. when going for a walk or having sports) as well. Within this paper we will introduce a novel architecture for distributed AAL solutions whose design follows a modern Microservices approach by providing small core services instead of a monolithic application framework. The architecture comprises core services for sensor integration, and service discovery while supporting several communication models (periodic, sporadic, streaming). We extend the state-of-the-art by introducing a fault-tolerance model for our architecture on the basis of a fault-hypothesis describing the fault-containment regions (FCRs) with their respective failure modes and failure rates in order to support safety-critical AAL applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fault detection and diagnosis using neural network approaches

NASA Technical Reports Server (NTRS)

Kramer, Mark A.

1992-01-01

Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

Applications of Fault Detection in Vibrating Structures

NASA Technical Reports Server (NTRS)

Eure, Kenneth W.; Hogge, Edward; Quach, Cuong C.; Vazquez, Sixto L.; Russell, Andrew; Hill, Boyd L.

2012-01-01

Structural fault detection and identification remains an area of active research. Solutions to fault detection and identification may be based on subtle changes in the time series history of vibration signals originating from various sensor locations throughout the structure. The purpose of this paper is to document the application of vibration based fault detection methods applied to several structures. Overall, this paper demonstrates the utility of vibration based methods for fault detection in a controlled laboratory setting and limitations of applying the same methods to a similar structure during flight on an experimental subscale aircraft.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Taking apart the Big Pine fault: Redefining a major structural feature in southern California

USGS Publications Warehouse

Onderdonk, N.W.; Minor, S.A.; Kellogg, K.S.

2005-01-01

New mapping along the Big Pine fault trend in southern California indicates that this structural alignment is actually three separate faults, which exhibit different geometries, slip histories, and senses of offset since Miocene time. The easternmost fault, along the north side of Lockwood Valley, exhibits left-lateral reverse Quaternary displacement but was a north dipping normal fault in late Oligocene to early Miocene time. The eastern Big Pine fault that bounds the southern edge of the Cuyama Badlands is a south dipping reverse fault that is continuous with the San Guillermo fault. The western segment of the Big Pine fault trend is a north dipping thrust fault continuous with the Pine Mountain fault and delineates the northern boundary of the rotated western Transverse Ranges terrane. This redefinition of the Big Pine fault differs greatly from the previous interpretation and significantly alters regional tectonic models and seismic risk estimates. The outcome of this study also demonstrates that basic geologic mapping is still needed to support the development of geologic models. Copyright 2005 by the American Geophysical Union.

Extended Testability Analysis Tool

NASA Technical Reports Server (NTRS)

Melcher, Kevin; Maul, William A.; Fulton, Christopher

2012-01-01

The Extended Testability Analysis (ETA) Tool is a software application that supports fault management (FM) by performing testability analyses on the fault propagation model of a given system. Fault management includes the prevention of faults through robust design margins and quality assurance methods, or the mitigation of system failures. Fault management requires an understanding of the system design and operation, potential failure mechanisms within the system, and the propagation of those potential failures through the system. The purpose of the ETA Tool software is to process the testability analysis results from a commercial software program called TEAMS Designer in order to provide a detailed set of diagnostic assessment reports. The ETA Tool is a command-line process with several user-selectable report output options. The ETA Tool also extends the COTS testability analysis and enables variation studies with sensor sensitivity impacts on system diagnostics and component isolation using a single testability output. The ETA Tool can also provide extended analyses from a single set of testability output files. The following analysis reports are available to the user: (1) the Detectability Report provides a breakdown of how each tested failure mode was detected, (2) the Test Utilization Report identifies all the failure modes that each test detects, (3) the Failure Mode Isolation Report demonstrates the system s ability to discriminate between failure modes, (4) the Component Isolation Report demonstrates the system s ability to discriminate between failure modes relative to the components containing the failure modes, (5) the Sensor Sensor Sensitivity Analysis Report shows the diagnostic impact due to loss of sensor information, and (6) the Effect Mapping Report identifies failure modes that result in specified system-level effects.

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

PubMed Central

He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

2018-01-01

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers. PMID:29690641

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines.

PubMed

He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

2018-04-23

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

A residual based adaptive unscented Kalman filter for fault recovery in attitude determination system of microsatellites

NASA Astrophysics Data System (ADS)

Le, Huy Xuan; Matunaga, Saburo

2014-12-01

This paper presents an adaptive unscented Kalman filter (AUKF) to recover the satellite attitude in a fault detection and diagnosis (FDD) subsystem of microsatellites. The FDD subsystem includes a filter and an estimator with residual generators, hypothesis tests for fault detections and a reference logic table for fault isolations and fault recovery. The recovery process is based on the monitoring of mean and variance values of each attitude sensor behaviors from residual vectors. In the case of normal work, the residual vectors should be in the form of Gaussian white noise with zero mean and fixed variance. When the hypothesis tests for the residual vectors detect something unusual by comparing the mean and variance values with dynamic thresholds, the AUKF with real-time updated measurement noise covariance matrix will be used to recover the sensor faults. The scheme developed in this paper resolves the problem of the heavy and complex calculations during residual generations and therefore the delay in the isolation process is reduced. The numerical simulations for TSUBAME, a demonstration microsatellite of Tokyo Institute of Technology, are conducted and analyzed to demonstrate the working of the AUKF and FDD subsystem.

Aircraft engine sensor fault diagnostics using an on-line OBEM update method.

PubMed

Liu, Xiaofeng; Xue, Naiyu; Yuan, Ye

2017-01-01

This paper proposed a method to update the on-line health reference baseline of the On-Board Engine Model (OBEM) to maintain the effectiveness of an in-flight aircraft sensor Fault Detection and Isolation (FDI) system, in which a Hybrid Kalman Filter (HKF) was incorporated. Generated from a rapid in-flight engine degradation, a large health condition mismatch between the engine and the OBEM can corrupt the performance of the FDI. Therefore, it is necessary to update the OBEM online when a rapid degradation occurs, but the FDI system will lose estimation accuracy if the estimation and update are running simultaneously. To solve this problem, the health reference baseline for a nonlinear OBEM was updated using the proposed channel controller method. Simulations based on the turbojet engine Linear-Parameter Varying (LPV) model demonstrated the effectiveness of the proposed FDI system in the presence of substantial degradation, and the channel controller can ensure that the update process finishes without interference from a single sensor fault.

Aircraft engine sensor fault diagnostics using an on-line OBEM update method

PubMed Central

Liu, Xiaofeng; Xue, Naiyu; Yuan, Ye

2017-01-01

This paper proposed a method to update the on-line health reference baseline of the On-Board Engine Model (OBEM) to maintain the effectiveness of an in-flight aircraft sensor Fault Detection and Isolation (FDI) system, in which a Hybrid Kalman Filter (HKF) was incorporated. Generated from a rapid in-flight engine degradation, a large health condition mismatch between the engine and the OBEM can corrupt the performance of the FDI. Therefore, it is necessary to update the OBEM online when a rapid degradation occurs, but the FDI system will lose estimation accuracy if the estimation and update are running simultaneously. To solve this problem, the health reference baseline for a nonlinear OBEM was updated using the proposed channel controller method. Simulations based on the turbojet engine Linear-Parameter Varying (LPV) model demonstrated the effectiveness of the proposed FDI system in the presence of substantial degradation, and the channel controller can ensure that the update process finishes without interference from a single sensor fault. PMID:28182692

Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors — A Comparative Study

PubMed Central

Qu, Yongzhi; He, David; Yoon, Jae; Van Hecke, Brandon; Bechhoefer, Eric; Zhu, Junda

2014-01-01

In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance. PMID:24424467

GPS-aided inertial technology and navigation-based photogrammetry for aerial mapping the San Andreas fault system

USGS Publications Warehouse

Sanchez, Richard D.; Hudnut, Kenneth W.

2004-01-01

Aerial mapping of the San Andreas Fault System can be realized more efficiently and rapidly without ground control and conventional aerotriangulation. This is achieved by the direct geopositioning of the exterior orientation of a digital imaging sensor by use of an integrated Global Positioning System (GPS) receiver and an Inertial Navigation System (INS). A crucial issue to this particular type of aerial mapping is the accuracy, scale, consistency, and speed achievable by such a system. To address these questions, an Applanix Digital Sensor System (DSS) was used to examine its potential for near real-time mapping. Large segments of vegetation along the San Andreas and Cucamonga faults near the foothills of the San Bernardino and San Gabriel Mountains were burned to the ground in the California wildfires of October-November 2003. A 175 km corridor through what once was a thickly vegetated and hidden fault surface was chosen for this study. Both faults pose a major hazard to the greater Los Angeles metropolitan area and a near real-time mapping system could provide information vital to a post-disaster response.

Multivariate EMD and full spectrum based condition monitoring for rotating machinery

NASA Astrophysics Data System (ADS)

Zhao, Xiaomin; Patel, Tejas H.; Zuo, Ming J.

2012-02-01

Early assessment of machinery health condition is of paramount importance today. A sensor network with sensors in multiple directions and locations is usually employed for monitoring the condition of rotating machinery. Extraction of health condition information from these sensors for effective fault detection and fault tracking is always challenging. Empirical mode decomposition (EMD) is an advanced signal processing technology that has been widely used for this purpose. Standard EMD has the limitation in that it works only for a single real-valued signal. When dealing with data from multiple sensors and multiple health conditions, standard EMD faces two problems. First, because of the local and self-adaptive nature of standard EMD, the decomposition of signals from different sources may not match in either number or frequency content. Second, it may not be possible to express the joint information between different sensors. The present study proposes a method of extracting fault information by employing multivariate EMD and full spectrum. Multivariate EMD can overcome the limitations of standard EMD when dealing with data from multiple sources. It is used to extract the intrinsic mode functions (IMFs) embedded in raw multivariate signals. A criterion based on mutual information is proposed for selecting a sensitive IMF. A full spectral feature is then extracted from the selected fault-sensitive IMF to capture the joint information between signals measured from two orthogonal directions. The proposed method is first explained using simple simulated data, and then is tested for the condition monitoring of rotating machinery applications. The effectiveness of the proposed method is demonstrated through monitoring damage on the vane trailing edge of an impeller and rotor-stator rub in an experimental rotor rig.

Compressive sensing-based electrostatic sensor array signal processing and exhausted abnormal debris detecting

NASA Astrophysics Data System (ADS)

Tang, Xin; Chen, Zhongsheng; Li, Yue; Yang, Yongmin

2018-05-01

When faults happen at gas path components of gas turbines, some sparsely-distributed and charged debris will be generated and released into the exhaust gas. The debris is called abnormal debris. Electrostatic sensors can detect the debris online and further indicate the faults. It is generally considered that, under a specific working condition, a more serious fault generates more and larger debris, and a piece of larger debris carries more charge. Therefore, the amount and charge of the abnormal debris are important indicators of the fault severity. However, because an electrostatic sensor can only detect the superposed effect on the electrostatic field of all the debris, it can hardly identify the amount and position of the debris. Moreover, because signals of electrostatic sensors depend on not only charge but also position of debris, and the position information is difficult to acquire, measuring debris charge accurately using the electrostatic detecting method is still a technical difficulty. To solve these problems, a hemisphere-shaped electrostatic sensors' circular array (HSESCA) is used, and an array signal processing method based on compressive sensing (CS) is proposed in this paper. To research in a theoretical framework of CS, the measurement model of the HSESCA is discretized into a sparse representation form by meshing. In this way, the amount and charge of the abnormal debris are described as a sparse vector. It is further reconstructed by constraining l1-norm when solving an underdetermined equation. In addition, a pre-processing method based on singular value decomposition and a result calibration method based on weighted-centroid algorithm are applied to ensure the accuracy of the reconstruction. The proposed method is validated by both numerical simulations and experiments. Reconstruction errors, characteristics of the results and some related factors are discussed.

Developing a Hayward Fault Greenbelt in Fremont, California

NASA Astrophysics Data System (ADS)

Blueford, J. R.

2007-12-01

The Math Science Nucleus, an educational non-profit, in cooperation with the City of Fremont and U.S. Geological Survey has concluded that outdoor and indoor exhibits highlighting the Hayward Fault is a spectacular and educational way of illustrating the power of earthquakes. Several projects are emerging that use the Hayward fault to illustrate to the public and school groups that faults mold the landscape upon which they live. One area that is already developed, Tule Ponds at Tyson Lagoon, is owned by Alameda County Flood Control and Conservation District and managed by the Math Science Nucleus. This 17 acre site illustrates two traces of the Hayward fault (active and inactive), whose sediments record over 4000 years of activity. Another project is selecting an area in Fremont that a permanent trench or outside earthquake exhibit can be created that people can see seismic stratigraphic features of the Hayward Fault. This would be part of a 3 mile Earthquake Greenbelt area from Tyson Lagoon to the proposed Irvington BART Station. Informational kiosks or markers and a "yellow brick road" of earthquake facts could allow visitors to take an exciting and educational tour of the Hayward Fault's surface features in Fremont. Visitors would visually see the effects of fault movement and the tours would include preparedness information. As these plans emerge, an indoor permanent exhibits is being developed at the Children's Natural History Museum in Fremont. This exhibit will be a model of the Earthquake Greenbelt. It will also allow people to see a scale model of how the Hayward Fault unearthed the Pleistocene fossil bed (Irvingtonian) as well as created traps for underground aquifers as well as surface sag ponds.

Gyro-based Maximum-Likelihood Thruster Fault Detection and Identification

NASA Technical Reports Server (NTRS)

Wilson, Edward; Lages, Chris; Mah, Robert; Clancy, Daniel (Technical Monitor)

2002-01-01

When building smaller, less expensive spacecraft, there is a need for intelligent fault tolerance vs. increased hardware redundancy. If fault tolerance can be achieved using existing navigation sensors, cost and vehicle complexity can be reduced. A maximum likelihood-based approach to thruster fault detection and identification (FDI) for spacecraft is developed here and applied in simulation to the X-38 space vehicle. The system uses only gyro signals to detect and identify hard, abrupt, single and multiple jet on- and off-failures. Faults are detected within one second and identified within one to five accords,

A Flight Expert System (FLES) For On-Board Fault Monitoring And Diagnosis

NASA Astrophysics Data System (ADS)

Ali, M.; Scharnhorst, D...; Ai, C. S.; Ferber, H. J.

1986-03-01

The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher work loads for the pilot and tend to induce a higher probability of errors. Therefore, we have developed an architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing and recovering from in-flight faults. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with the airplane status information during the diagnostic process. The simulator is based partly on the Lockheed Advanced Concept System (ACS), a future generation airplane, and partly on the Boeing 737, an existing airplane. A distinction between two types of faults, maladjustments and malfunctions, has led us to take two approaches to fault diagnosis. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to assess abnormal situations during airplane flight. This paper describes our reasons for handling malfunctions and maladjustments separately and the use of domain knowledge in the diagnosis of each.

A flight expert system (FLES) for on-board fault monitoring and diagnosis

NASA Technical Reports Server (NTRS)

Ali, M.; Scharnhorst, D. A.; Ai, C. S.; Ferber, H. J.

1986-01-01

The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher work loads for the pilot and tend to induce a higher probability of errors. Therefore, an architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing and recovering from in-flight faults has been developed. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with the airplane status information during the diagnostic process. The simulator is based partly on the Lockheed Advanced Concept System (ACS), a future generation airplane, and partly on the Boeing 737, an existing airplane. A distinction between two types of faults, maladjustments and malfunctions, has led us to take two approaches to fault diagnosis. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to assess abnormal situations during airplane flight. This paper describes the reasons for handling malfunctions and maladjustments separately and the use of domain knowledge in the diagnosis of each.

Sensor Selection and Data Validation for Reliable Integrated System Health Management

NASA Technical Reports Server (NTRS)

Garg, Sanjay; Melcher, Kevin J.

2008-01-01

For new access to space systems with challenging mission requirements, effective implementation of integrated system health management (ISHM) must be available early in the program to support the design of systems that are safe, reliable, highly autonomous. Early ISHM availability is also needed to promote design for affordable operations; increased knowledge of functional health provided by ISHM supports construction of more efficient operations infrastructure. Lack of early ISHM inclusion in the system design process could result in retrofitting health management systems to augment and expand operational and safety requirements; thereby increasing program cost and risk due to increased instrumentation and computational complexity. Having the right sensors generating the required data to perform condition assessment, such as fault detection and isolation, with a high degree of confidence is critical to reliable operation of ISHM. Also, the data being generated by the sensors needs to be qualified to ensure that the assessments made by the ISHM is not based on faulty data. NASA Glenn Research Center has been developing technologies for sensor selection and data validation as part of the FDDR (Fault Detection, Diagnosis, and Response) element of the Upper Stage project of the Ares 1 launch vehicle development. This presentation will provide an overview of the GRC approach to sensor selection and data quality validation and will present recent results from applications that are representative of the complexity of propulsion systems for access to space vehicles. A brief overview of the sensor selection and data quality validation approaches is provided below. The NASA GRC developed Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively selecting an optimal sensor suite to provide overall health assessment of a host system. S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.

Basic research on machinery fault diagnostics: Past, present, and future trends

NASA Astrophysics Data System (ADS)

Chen, Xuefeng; Wang, Shibin; Qiao, Baijie; Chen, Qiang

2018-06-01

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines

PubMed Central

Liu, Liansheng; Liu, Datong; Zhang, Yujie; Peng, Yu

2016-01-01

In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved. PMID:27136561

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines.

PubMed

Liu, Liansheng; Liu, Datong; Zhang, Yujie; Peng, Yu

2016-04-29

In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved.

KEA-71 Smart Current Signature Sensor (SCSS)

NASA Technical Reports Server (NTRS)

Perotti, Jose M.

2010-01-01

This slide presentation reviews the development and uses of the Smart Current Signature Sensor (SCSS), also known as the Valve Health Monitor (VHM) system. SCSS provides a way to not only monitor real-time the valve's operation in a non invasive manner, but also to monitor its health (Fault Detection and Isolation) and identify potential faults and/or degradation in the near future (Prediction/Prognosis). This technology approach is not only applicable for solenoid valves, and it could be extrapolated to other electrical components with repeatable electrical current signatures such as motors.

Fault diagnosis

NASA Technical Reports Server (NTRS)

Abbott, Kathy

1990-01-01

The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.

Development of the Elastic Rebound Strike-slip (ERS) Fault Model for Teaching Earthquake Science to Non-science Students

NASA Astrophysics Data System (ADS)

Glesener, G. B.; Peltzer, G.; Stubailo, I.; Cochran, E. S.; Lawrence, J. F.

2009-12-01

The Modeling and Educational Demonstrations Laboratory (MEDL) at the University of California, Los Angeles has developed a fourth version of the Elastic Rebound Strike-slip (ERS) Fault Model to be used to educate students and the general public about the process and mechanics of earthquakes from strike-slip faults. The ERS Fault Model is an interactive hands-on teaching tool which produces failure on a predefined fault embedded in an elastic medium, with adjustable normal stress. With the addition of an accelerometer sensor, called the Joy Warrior, the user can experience what it is like for a field geophysicist to collect and observe ground shaking data from an earthquake without having to experience a real earthquake. Two knobs on the ERS Fault Model control the normal and shear stress on the fault. Adjusting the normal stress knob will increase or decrease the friction on the fault. The shear stress knob displaces one side of the elastic medium parallel to the strike of the fault, resulting in changing shear stress on the fault surface. When the shear stress exceeds the threshold defined by the static friction of the fault, an earthquake on the model occurs. The accelerometer sensor then sends the data to a computer where the shaking of the model due to the sudden slip on the fault can be displayed and analyzed by the student. The experiment clearly illustrates the relationship between earthquakes and seismic waves. One of the major benefits to using the ERS Fault Model in undergraduate courses is that it helps to connect non-science students with the work of scientists. When students that are not accustomed to scientific thought are able to experience the scientific process first hand, a connection is made between the scientists and students. Connections like this might inspire a student to become a scientist, or promote the advancement of scientific research through public policy.

Fault Tolerance in ZigBee Wireless Sensor Networks

NASA Technical Reports Server (NTRS)

Alena, Richard; Gilstrap, Ray; Baldwin, Jarren; Stone, Thom; Wilson, Pete

2011-01-01

Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 PRO Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. This technology is supported by System-on-a-Chip solutions, resulting in extremely small and low-power nodes. The Wireless Connections in Space Project addresses the aerospace flight domain for both flight-critical and non-critical avionics. WSNs provide the inherent fault tolerance required for aerospace applications utilizing such technology. The team from Ames Research Center has developed techniques for assessing the fault tolerance of ZigBee WSNs challenged by radio frequency (RF) interference or WSN node failure.

Evolution of Friction, Wear, and Seismic Radiation Along Experimental Bi-material Faults

NASA Astrophysics Data System (ADS)

Carpenter, B. M.; Zu, X.; Shadoan, T.; Self, A.; Reches, Z.

2017-12-01

Faults are commonly composed by rocks of different lithologies and mechanical properties that are positioned against one another by fault slip; such faults are referred to as bimaterial-faults (BF). We investigate the mechanical behavior, wear production, and seismic radiation of BF via laboratory experiments on a rotary shear apparatus. In the experiments, two rock blocks of dissimilar or similar lithology are sheared against each other. We used contrasting rock pairs of a stiff, igneous block (diorite, granite, or gabbro) against a more compliant, sedimentary block (sandstone, limestone, or dolomite). The cylindrical blocks have a ring-shaped contact, and are loaded under conditions of constant normal stress and shear velocity. Fault behavior was monitored with stress, velocity and dilation sensors. Acoustic activity is monitored with four 3D accelerometers mounted at 2 cm distance from the experimental fault. These sensors can measure accelerations up to 500 g, and their full waveform output is recorded at 1MHz for periods up to 14 sec. Our preliminary results indicate that the bi-material nature of the fault has a strong affect on slip initiation, wear evolution, and acoustic emission activity. In terms of wear, we observe enhanced wear in experiments with a sandstone block sheared against a gabbro or limestone block. Experiments with a limestone or sandstone block produced distinct slickenline striations. Further, significant differences appeared in the number and amplitude of acoustic events depending on the bi-material setting and slip-distance. A gabbro-gabbro fault showed a decrease in both amplitude and number of acoustic events with increasing slip. Conversely, a gabbro-limestone fault showed a decrease in the number of events, but an increase in average event amplitude. Ongoing work focuses on advanced characterization of mechanical, dynamic weakening, and acoustic, frequency content, parameters.

Neural network application to comprehensive engine diagnostics

NASA Technical Reports Server (NTRS)

Marko, Kenneth A.

1994-01-01

We have previously reported on the use of neural networks for detection and identification of faults in complex microprocessor controlled powertrain systems. The data analyzed in those studies consisted of the full spectrum of signals passing between the engine and the real-time microprocessor controller. The specific task of the classification system was to classify system operation as nominal or abnormal and to identify the fault present. The primary concern in earlier work was the identification of faults, in sensors or actuators in the powertrain system as it was exercised over its full operating range. The use of data from a variety of sources, each contributing some potentially useful information to the classification task, is commonly referred to as sensor fusion and typifies the type of problems successfully addressed using neural networks. In this work we explore the application of neural networks to a different diagnostic problem, the diagnosis of faults in newly manufactured engines and the utility of neural networks for process control.

Planetary Gearbox Fault Diagnosis Using a Single Piezoelectric Strain Sensor

DTIC Science & Technology

2014-12-23

However, the fault detection of planetary gearbox is very complicate since the c omplex nature of dynamic rolling structure of p lanetary gearbox...vibration transfer paths due to the unique dynamic structure of rotating planet gears. Therefore, it is difficult to diagnose PGB faults via vibration...al. 2014). To overcome the above mentioned challenges in developing effective PGB fau lt diagnosis capability , a research investigation on

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

Fault kinematics and localised inversion within the Troms-Finnmark Fault Complex, SW Barents Sea

NASA Astrophysics Data System (ADS)

Zervas, I.; Omosanya, K. O.; Lippard, S. J.; Johansen, S. E.

2018-04-01

The areas bounding the Troms-Finnmark Fault Complex are affected by complex tectonic evolution. In this work, the history of fault growth, reactivation, and inversion of major faults in the Troms-Finnmark Fault Complex and the Ringvassøy Loppa Fault Complex is interpreted from three-dimensional seismic data, structural maps and fault displacement plots. Our results reveal eight normal faults bounding rotated fault blocks in the Troms-Finnmark Fault Complex. Both the throw-depth and displacement-distance plots show that the faults exhibit complex configurations of lateral and vertical segmentation with varied profiles. Some of the faults were reactivated by dip-linkages during the Late Jurassic and exhibit polycyclic fault growth, including radial, syn-sedimentary, and hybrid propagation. Localised positive inversion is the main mechanism of fault reactivation occurring at the Troms-Finnmark Fault Complex. The observed structural styles include folds associated with extensional faults, folded growth wedges and inverted depocentres. Localised inversion was intermittent with rifting during the Middle Jurassic-Early Cretaceous at the boundaries of the Troms-Finnmark Fault Complex to the Finnmark Platform. Additionally, tectonic inversion was more intense at the boundaries of the two fault complexes, affecting Middle Triassic to Early Cretaceous strata. Our study shows that localised folding is either a product of compressional forces or of lateral movements in the Troms-Finnmark Fault Complex. Regional stresses due to the uplift in the Loppa High and halokinesis in the Tromsø Basin are likely additional causes of inversion in the Troms-Finnmark Fault Complex.

Tectonic fault monitoring at open pit mine at Zarnitsa Kimberlite Pipe

NASA Astrophysics Data System (ADS)

Vostrikov, VI; Polotnyanko, NS; Trofimov, AS; Potaka, AA

2018-03-01

The article describes application of Karier instrumentation designed at the Institute of Mining to study fracture formation in rocks. The instrumentation composed of three sensors was used to control widening of a tectonic fault intersecting an open pit mine at Zarnitsa Kimberlite Pipe in Yakutia. The monitoring between 28 November and 28 December in 2016 recorded convergence of the fault walls from one side of the open pit mine and widening from the other side. After production blasts, the fault first grows in width and then recovers.

Implementation of Integrated System Fault Management Capability

NASA Technical Reports Server (NTRS)

Figueroa, Fernando; Schmalzel, John; Morris, Jon; Smith, Harvey; Turowski, Mark

2008-01-01

Fault Management to support rocket engine test mission with highly reliable and accurate measurements; while improving availability and lifecycle costs. CORE ELEMENTS: Architecture, taxonomy, and ontology (ATO) for DIaK management. Intelligent Sensor Processes; Intelligent Element Processes; Intelligent Controllers; Intelligent Subsystem Processes; Intelligent System Processes; Intelligent Component Processes.

Flight elements: Fault detection and fault management

NASA Technical Reports Server (NTRS)

Lum, H.; Patterson-Hine, A.; Edge, J. T.; Lawler, D.

1990-01-01

Fault management for an intelligent computational system must be developed using a top down integrated engineering approach. An approach proposed includes integrating the overall environment involving sensors and their associated data; design knowledge capture; operations; fault detection, identification, and reconfiguration; testability; causal models including digraph matrix analysis; and overall performance impacts on the hardware and software architecture. Implementation of the concept to achieve a real time intelligent fault detection and management system will be accomplished via the implementation of several objectives, which are: Development of fault tolerant/FDIR requirement and specification from a systems level which will carry through from conceptual design through implementation and mission operations; Implementation of monitoring, diagnosis, and reconfiguration at all system levels providing fault isolation and system integration; Optimize system operations to manage degraded system performance through system integration; and Lower development and operations costs through the implementation of an intelligent real time fault detection and fault management system and an information management system.

High-Intensity Radiated Field Fault-Injection Experiment for a Fault-Tolerant Distributed Communication System

NASA Technical Reports Server (NTRS)

Yates, Amy M.; Torres-Pomales, Wilfredo; Malekpour, Mahyar R.; Gonzalez, Oscar R.; Gray, W. Steven

2010-01-01

Safety-critical distributed flight control systems require robustness in the presence of faults. In general, these systems consist of a number of input/output (I/O) and computation nodes interacting through a fault-tolerant data communication system. The communication system transfers sensor data and control commands and can handle most faults under typical operating conditions. However, the performance of the closed-loop system can be adversely affected as a result of operating in harsh environments. In particular, High-Intensity Radiated Field (HIRF) environments have the potential to cause random fault manifestations in individual avionic components and to generate simultaneous system-wide communication faults that overwhelm existing fault management mechanisms. This paper presents the design of an experiment conducted at the NASA Langley Research Center's HIRF Laboratory to statistically characterize the faults that a HIRF environment can trigger on a single node of a distributed flight control system.

Quantitative Index and Abnormal Alarm Strategy Using Sensor-Dependent Vibration Data for Blade Crack Identification in Centrifugal Booster Fans

PubMed Central

Chen, Jinglong; Sun, Hailiang; Wang, Shuai; He, Zhengjia

2016-01-01

Centrifugal booster fans are important equipment used to recover blast furnace gas (BFG) for generating electricity, but blade crack faults (BCFs) in centrifugal booster fans can lead to unscheduled breakdowns and potentially serious accidents, so in this work quantitative fault identification and an abnormal alarm strategy based on acquired historical sensor-dependent vibration data is proposed for implementing condition-based maintenance for this type of equipment. Firstly, three group dependent sensors are installed to acquire running condition data. Then a discrete spectrum interpolation method and short time Fourier transform (STFT) are applied to preliminarily identify the running data in the sensor-dependent vibration data. As a result a quantitative identification and abnormal alarm strategy based on compound indexes including the largest Lyapunov exponent and relative energy ratio at the second harmonic frequency component is proposed. Then for validation the proposed blade crack quantitative identification and abnormality alarm strategy is applied to analyze acquired experimental data for centrifugal booster fans and it has successfully identified incipient blade crack faults. In addition, the related mathematical modelling work is also introduced to investigate the effects of mistuning and cracks on the vibration features of centrifugal impellers and to explore effective techniques for crack detection. PMID:27171083

Robust sensor fault detection and isolation of gas turbine engines subjected to time-varying parameter uncertainties

NASA Astrophysics Data System (ADS)

Pourbabaee, Bahareh; Meskin, Nader; Khorasani, Khashayar

2016-08-01

In this paper, a novel robust sensor fault detection and isolation (FDI) strategy using the multiple model-based (MM) approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise in all the channels. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple piecewise linear (PWL) models that are constructed at various operating points of an uncertain nonlinear system. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (AREs) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The proposed multiple RKF-based FDI scheme is simulated for a single spool gas turbine engine to diagnose various sensor faults despite the presence of parameter uncertainties, process and measurement noise. Our comparative studies confirm the superiority of our proposed FDI method when compared to the methods that are available in the literature.

Monitoring and Control Interface Based on Virtual Sensors

PubMed Central

Escobar, Ricardo F.; Adam-Medina, Manuel; García-Beltrán, Carlos D.; Olivares-Peregrino, Víctor H.; Juárez-Romero, David; Guerrero-Ramírez, Gerardo V.

2014-01-01

In this article, a toolbox based on a monitoring and control interface (MCI) is presented and applied in a heat exchanger. The MCI was programed in order to realize sensor fault detection and isolation and fault tolerance using virtual sensors. The virtual sensors were designed from model-based high-gain observers. To develop the control task, different kinds of control laws were included in the monitoring and control interface. These control laws are PID, MPC and a non-linear model-based control law. The MCI helps to maintain the heat exchanger under operation, even if a temperature outlet sensor fault occurs; in the case of outlet temperature sensor failure, the MCI will display an alarm. The monitoring and control interface is used as a practical tool to support electronic engineering students with heat transfer and control concepts to be applied in a double-pipe heat exchanger pilot plant. The method aims to teach the students through the observation and manipulation of the main variables of the process and by the interaction with the monitoring and control interface (MCI) developed in LabVIEW©. The MCI provides the electronic engineering students with the knowledge of heat exchanger behavior, since the interface is provided with a thermodynamic model that approximates the temperatures and the physical properties of the fluid (density and heat capacity). An advantage of the interface is the easy manipulation of the actuator for an automatic or manual operation. Another advantage of the monitoring and control interface is that all algorithms can be manipulated and modified by the users. PMID:25365462

A fuzzy Petri-net-based mode identification algorithm for fault diagnosis of complex systems

NASA Astrophysics Data System (ADS)

Propes, Nicholas C.; Vachtsevanos, George

2003-08-01

Complex dynamical systems such as aircraft, manufacturing systems, chillers, motor vehicles, submarines, etc. exhibit continuous and event-driven dynamics. These systems undergo several discrete operating modes from startup to shutdown. For example, a certain shipboard system may be operating at half load or full load or may be at start-up or shutdown. Of particular interest are extreme or "shock" operating conditions, which tend to severely impact fault diagnosis or the progression of a fault leading to a failure. Fault conditions are strongly dependent on the operating mode. Therefore, it is essential that in any diagnostic/prognostic architecture, the operating mode be identified as accurately as possible so that such functions as feature extraction, diagnostics, prognostics, etc. can be correlated with the predominant operating conditions. This paper introduces a mode identification methodology that incorporates both time- and event-driven information about the process. A fuzzy Petri net is used to represent the possible successive mode transitions and to detect events from processed sensor signals signifying a mode change. The operating mode is initialized and verified by analysis of the time-driven dynamics through a fuzzy logic classifier. An evidence combiner module is used to combine the results from both the fuzzy Petri net and the fuzzy logic classifier to determine the mode. Unlike most event-driven mode identifiers, this architecture will provide automatic mode initialization through the fuzzy logic classifier and robustness through the combining of evidence of the two algorithms. The mode identification methodology is applied to an AC Plant typically found as a component of a shipboard system.

Earthquake geology and paleoseismology of major strands of the San Andreas fault system: Chapter 38

USGS Publications Warehouse

Rockwell, Thomas; Scharer, Katherine M.; Dawson, Timothy E.

2016-01-01

The San Andreas fault system in California is one of the best-studied faults in the world, both in terms of the long-term geologic history and paleoseismic study of past surface ruptures. In this paper, we focus on the Quaternary to historic data that have been collected from the major strands of the San Andreas fault system, both on the San Andreas Fault itself, and the major subparallel strands that comprise the plate boundary, including the Calaveras-Hayward- Rogers Creek-Maacama fault zone and the Concord-Green Valley-Bartlett Springs fault zone in northern California, and the San Jacinto and Elsinore faults in southern California. The majority of the relative motion between the Pacific and North American lithospheric plates is accommodated by these faults, with the San Andreas slipping at about 34 mm/yr in central California, decreasing to about 20 mm/yr in northern California north of its juncture with the Calaveras and Concord faults. The Calaveras-Hayward-Rogers Creek-Maacama fault zone exhibits a slip rate of 10-15 mm/yr, whereas the rate along the Concord-Green Valley-Bartlett Springs fault zone is lower at about 5 mm/yr. In southern California, the San Andreas exhibits a slip rate of about 35 mm/yr along the Mojave section, decreasing to as low as 10-15 mm/yr along its juncture with the San Jacinto fault, and about 20 mm/yr in the Coachella Valley. The San Jacinto and Elsinore fault zones exhibit rates of about 15 and 5 mm/yr, respectively. The average recurrence interval for surface-rupturing earthquakes along individual elements of the San Andreas fault system range from 100-500 years and is consistent with slip rate at those sites: higher slip rates produce more frequent or larger earthquakes. There is also evidence of short-term variations in strain release (slip rate) along various fault sections, as expressed as “flurries” or clusters of earthquakes as well as periods of relatively fewer surface ruptures in these relatively short records. This is reflected by non-periodic coefficients of variation in earthquake recurrence of 0.4 to 0.7 for the various paleoseismic sites.

Inspection of Piezoceramic Transducers Used for Structural Health Monitoring

PubMed Central

Mueller, Inka; Fritzen, Claus-Peter

2017-01-01

The use of piezoelectric wafer active sensors (PWAS) for structural health monitoring (SHM) purposes is state of the art for acousto-ultrasonic-based methods. For system reliability, detailed information about the PWAS itself is necessary. This paper gives an overview on frequent PWAS faults and presents the effects of these faults on the wave propagation, used for active acousto-ultrasonics-based SHM. The analysis of the wave field is based on velocity measurements using a laser Doppler vibrometer (LDV). New and established methods of PWAS inspection are explained in detail, listing advantages and disadvantages. The electro-mechanical impedance spectrum as basis for these methods is discussed for different sensor faults. This way this contribution focuses on a detailed analysis of PWAS and the need of their inspection for an increased reliability of SHM systems. PMID:28772431

A simple approach to detect and correct signal faults of Hall position sensors for brushless DC motors at steady speed

NASA Astrophysics Data System (ADS)

Shi, Yongli; Wu, Zhong; Zhi, Kangyi; Xiong, Jun

2018-03-01

In order to realize reliable commutation of brushless DC motors (BLDCMs), a simple approach is proposed to detect and correct signal faults of Hall position sensors in this paper. First, the time instant of the next jumping edge for Hall signals is predicted by using prior information of pulse intervals in the last electrical period. Considering the possible errors between the predicted instant and the real one, a confidence interval is set by using the predicted value and a suitable tolerance for the next pulse edge. According to the relationship between the real pulse edge and the confidence interval, Hall signals can be judged and the signal faults can be corrected. Experimental results of a BLDCM at steady speed demonstrate the effectiveness of the approach.

A probabilistic method to diagnose faults of air handling units

NASA Astrophysics Data System (ADS)

Dey, Debashis

Air handling unit (AHU) is one of the most extensively used equipment in large commercial buildings. This device is typically customized and lacks quality system integration which can result in hardwire failures and controller errors. Air handling unit Performance Assessment Rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units. APAR is computationally simple enough that it can be embedded in commercial building automation and control systems and relies only upon sensor data and control signals that are commonly available in these systems. Although APAR has many advantages over other methods, for example no training data required and easy to implement commercially, most of the time it is unable to provide the diagnosis of the faults. For instance, a fault on temperature sensor could be fixed bias, drifting bias, inappropriate location, complete failure. Also a fault in mixing box can be return and outdoor damper leak or stuck. In addition, when multiple rules are satisfied the list of faults increases. There is no proper way to have the correct diagnosis for rule based fault detection system. To overcome this limitation we proposed Bayesian Belief Network (BBN) as a diagnostic tool. BBN can be used to simulate diagnostic thinking of FDD experts through a probabilistic way. In this study we developed a new way to detect and diagnose faults in AHU through combining APAR rules and Bayesian Belief network. Bayesian Belief Network is used as a decision support tool for rule based expert system. BBN is highly capable to prioritize faults when multiple rules are satisfied simultaneously. Also it can get information from previous AHU operating conditions and maintenance records to provide proper diagnosis. The proposed model is validated with real time measured data of a campus building at University of Texas at San Antonio (UTSA).The results show that BBN is correctly able to prioritize faults which can be verified by manual investigation.

Study of RF Propagation Characteristics for Wireless Sensor Networks in Railroad Environments

DOT National Transportation Integrated Search

2010-01-01

The freight railroad industry in North America is exerting efforts to leverage Wireless Sensor Networks to monitor systems and components on railcars. This allows fault detection and accident prevention even while a train is moving. Railcars, constru...

Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

NASA Astrophysics Data System (ADS)

Lu, Siliang; Wang, Xiaoxian; He, Qingbo; Liu, Fang; Liu, Yongbin

2016-12-01

Transient signal analysis (TSA) has been proven an effective tool for motor bearing fault diagnosis, but has yet to be applied in processing bearing fault signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for fault diagnosis under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the fault characteristic frequency is exposed in the envelope spectrum for bearing fault recognition. The TSAAR method can accurately estimate the phase information of the fault-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing fault diagnosis. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies.

Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger

NASA Astrophysics Data System (ADS)

Habbi, Hacene; Kidouche, Madjid; Kinnaert, Michel; Zelmat, Mimoun

2011-04-01

This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator faults.

Space shuttle main engine fault detection using neural networks

NASA Technical Reports Server (NTRS)

Bishop, Thomas; Greenwood, Dan; Shew, Kenneth; Stevenson, Fareed

1991-01-01

A method for on-line Space Shuttle Main Engine (SSME) anomaly detection and fault typing using a feedback neural network is described. The method involves the computation of features representing time-variance of SSME sensor parameters, using historical test case data. The network is trained, using backpropagation, to recognize a set of fault cases. The network is then able to diagnose new fault cases correctly. An essential element of the training technique is the inclusion of randomly generated data along with the real data, in order to span the entire input space of potential non-nominal data.

Nucleation and growth of strike slip faults in granite.

USGS Publications Warehouse

Segall, P.; Pollard, D.P.

1983-01-01

Fractures within granodiorite of the central Sierra Nevada, California, were studied to elucidate the mechanics of faulting in crystalline rocks, with emphasis on the nucleation of new fault surfaces and their subsequent propagation and growth. Within the study area the fractures form a single, subparallel array which strikes N50o-70oE and dips steeply to the S. Some of these fractures are identified as joints because displacements across the fracture surfaces exhibit dilation but no slip. The joints are filled with undeformed minerals, including epidote and chlorite. Other fractures are identified as small faults because they display left-lateral strike slip separations of up to 2m. Slickensides, developed on fault surfaces, plunge 0o-20o to the E. The faults occur parallel to, and in the same outcrop with, the joints. The faults are filled with epidote, chlorite, and quartz, which exhibit textural evidence of shear deformation. These observations indicate that the strike slip faults nucleated on earlier formed, mineral filled joints. Secondary, dilational fractures propagated from near the ends of some small faults contemporaneously with the left-lateral slip on the faults. These fractures trend 25o+ or -10o from the fault planes, parallel to the direction of inferred local maximum compressive stress. The faults did not propagate into intact rock in their own planes as shear fractures. -from Authors

Experiments in fault tolerant software reliability

NASA Technical Reports Server (NTRS)

Mcallister, David F.; Vouk, Mladen A.

1989-01-01

Twenty functionally equivalent programs were built and tested in a multiversion software experiment. Following unit testing, all programs were subjected to an extensive system test. In the process sixty-one distinct faults were identified among the versions. Less than 12 percent of the faults exhibited varying degrees of positive correlation. The common-cause (or similar) faults spanned as many as 14 components. However, a majority of these faults were trivial, and easily detected by proper unit and/or system testing. Only two of the seven similar faults were difficult faults, and both were caused by specification ambiguities. One of these faults exhibited variable identical-and-wrong response span, i.e. response span which varied with the testing conditions and input data. Techniques that could have been used to avoid the faults are discussed. For example, it was determined that back-to-back testing of 2-tuples could have been used to eliminate about 90 percent of the faults. In addition, four of the seven similar faults could have been detected by using back-to-back testing of 5-tuples. It is believed that most, if not all, similar faults could have been avoided had the specifications been written using more formal notation, the unit testing phase was subject to more stringent standards and controls, and better tools for measuring the quality and adequacy of the test data (e.g. coverage) were used.

Application of polymer-coated metal-insulator-semiconductor sensors for the detection of dissolved hydrogen

NASA Astrophysics Data System (ADS)

Li, Dongmei; Medlin, J. W.; Bastasz, R.

2006-06-01

The detection of dissolved hydrogen in liquids is crucial to many industrial applications, such as fault detection for oil-filled electrical equipment. To enhance the performance of metal-insulator-semiconductor (MIS) sensors for dissolved hydrogen detection, a palladium MIS sensor has been modified by depositing a polyimide (PI) layer above the palladium surface. Response measurements of the PI-coated sensors in mineral oil indicate that hydrogen is sensitively detected, while the effect of interfering gases on sensor response is minimized.

Performance analysis of a fault inferring nonlinear detection system algorithm with integrated avionics flight data

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

1985-01-01

This paper presents the performance analysis results of a fault inferring nonlinear detection system (FINDS) using integrated avionics sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. First, an overview of the FINDS algorithm structure is given. Then, aircraft state estimate time histories and statistics for the flight data sensors are discussed. This is followed by an explanation of modifications made to the detection and decision functions in FINDS to improve false alarm and failure detection performance. Next, the failure detection and false alarm performance of the FINDS algorithm are analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minutes of flight data. Results indicate that the detection speed, failure level estimation, and false alarm performance show a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed is faster for filter measurement sensors such as MLS than for filter input sensors such as flight control accelerometers. Finally, the progress in modifications of the FINDS algorithm design to accommodate flight computer constraints is discussed.

Using Fuzzy Clustering for Real-time Space Flight Safety

NASA Technical Reports Server (NTRS)

Lee, Charles; Haskell, Richard E.; Hanna, Darrin; Alena, Richard L.

2004-01-01

To ensure space flight safety, it is necessary to monitor myriad sensor readings on the ground and in flight. Since a space shuttle has many sensors, monitoring data and drawing conclusions from information contained within the data in real time is challenging. The nature of the information can be critical to the success of the mission and safety of the crew and therefore, must be processed with minimal data-processing time. Data analysis algorithms could be used to synthesize sensor readings and compare data associated with normal operation with the data obtained that contain fault patterns to draw conclusions. Detecting abnormal operation during early stages in the transition from safe to unsafe operation requires a large amount of historical data that can be categorized into different classes (non-risk, risk). Even though the 40 years of shuttle flight program has accumulated volumes of historical data, these data don t comprehensively represent all possible fault patterns since fault patterns are usually unknown before the fault occurs. This paper presents a method that uses a similarity measure between fuzzy clusters to detect possible faults in real time. A clustering technique based on a fuzzy equivalence relation is used to characterize temporal data. Data collected during an initial time period are separated into clusters. These clusters are characterized by their centroids. Clusters formed during subsequent time periods are either merged with an existing cluster or added to the cluster list. The resulting list of cluster centroids, called a cluster group, characterizes the behavior of a particular set of temporal data. The degree to which new clusters formed in a subsequent time period are similar to the cluster group is characterized by a similarity measure, q. This method is applied to downlink data from Columbia flights. The results show that this technique can detect an unexpected fault that has not been present in the training data set.

Seismicity preliminary results in a geothermal and volcano activity area: study case Liquiñe-Ofqui fault system in Southern Andes, Chile

NASA Astrophysics Data System (ADS)

Estay, N. P.; Yáñez Morroni, G.; Crempien, J. G. F.; Roquer, T.

2017-12-01

Fluid transport through the crust takes place in domains with high permeability. For this reason, fault damage zones are a main feature where fluids may circulate unimpeded, since they have much larger permeability than normal country rocks. With the location of earthquakes, it is possible to infer fault geometry and stress field of the crust, therefore we can determine potential places where fluid circualtion is taking place. With that purpose, we installed a seismic network in an active volcanic-geothermal system, the Liquiñe-Ofqui Fault System (LOFS), located in Puyuhuapi, Southern Andes (44°-45°S). This allowed to link epicentral seismicity, focal mechanisms and surface expression of fluid circulation (hot-springs and volcanos). The LOFS is composed by two NS-striking dextral master faults, and several secondary NE-striking dextral and normal faults. Surface manifestation of fluid circulation in Puyuhuapi area are: 1) six hot-springs, most of them spatially associated with different mapped faults; 2) seven minor eruptive centers aligned over a 10-km-along one of the master NS-striking fault, and; 3) the Melimouyu strato-volcano without any spatial relationship with mapped faults. The network consists of 6 short period seismometers (S31f-2.0a sensor of IESE, with natural frequency of 2Hz), that were installed between July 2016 and August 2017; also 4 permanent broad-band seismometers (Guralp 6TD/ CD 24 sensor) which belong to the Volcano Observatory of Southern Andes (OVDAS). Preliminary results show a correlation between seismicity and surface manifestation of fluid circulation. Seismicity has a heterogeneous distribution: most of the earthquake are concentrated is the master NS-striking fault with fluid circulation manifestations; however along the segments without surface manifestation of fluids do not have seismicity. These results suggest that fluid circulation mostly occur in areas with high seismicity, and thus, the increment in fluid pressure enhances fracturing and earthquake production.

Test plan. GCPS task 7, subtask 7.1: IHM development

NASA Technical Reports Server (NTRS)

Greenberg, H. S.

1994-01-01

The overall objective of Task 7 is to identify cost-effective life cycle integrated health management (IHM) approaches for a reusable launch vehicle's primary structure. Acceptable IHM approaches must: eliminate and accommodate faults through robust designs, identify optimum inspection/maintenance periods, automate ground and on-board test and check-out, and accommodate and detect structural faults by providing wide and localized area sensor and test coverage as required. These requirements are elements of our targeted primary structure low cost operations approach using airline-like maintenance by exception philosophies. This development plan will follow an evolutionary path paving the way to the ultimate development of flight-quality production, operations, and vehicle systems. This effort will be focused on maturing the recommended sensor technologies required for localized and wide area health monitoring to a technology readiness level (TRL) of 6 and to establish flight ready system design requirements. The following is a brief list of IHM program objectives: design out faults by analyzing material properties, structural geometry, and load and environment variables and identify failure modes and damage tolerance requirements; design in system robustness while meeting performance objectives (weight limitations) of the reusable launch vehicle primary structure; establish structural integrity margins to preclude the need for test and checkout and predict optimum inspection/maintenance periods through life prediction analysis; identify optimum fault protection system concept definitions combining system robustness and integrity margins established above with cost effective health monitoring technologies; and use coupons, panels, and integrated full scale primary structure test articles to identify, evaluate, and characterize the preferred NDE/NDI/IHM sensor technologies that will be a part of the fault protection system.

Review on the Traction System Sensor Technology of a Rail Transit Train.

PubMed

Feng, Jianghua; Xu, Junfeng; Liao, Wu; Liu, Yong

2017-06-11

The development of high-speed intelligent rail transit has increased the number of sensors applied on trains. These play an important role in train state control and monitoring. These sensors generally work in a severe environment, so the key problem for sensor data acquisition is to ensure data accuracy and reliability. In this paper, we follow the sequence of sensor signal flow, present sensor signal sensing technology, sensor data acquisition, and processing technology, as well as sensor fault diagnosis technology based on the voltage, current, speed, and temperature sensors which are commonly used in train traction systems. Finally, intelligent sensors and future research directions of rail transit train sensors are discussed.

Review on the Traction System Sensor Technology of a Rail Transit Train

PubMed Central

Feng, Jianghua; Xu, Junfeng; Liao, Wu; Liu, Yong

2017-01-01

The development of high-speed intelligent rail transit has increased the number of sensors applied on trains. These play an important role in train state control and monitoring. These sensors generally work in a severe environment, so the key problem for sensor data acquisition is to ensure data accuracy and reliability. In this paper, we follow the sequence of sensor signal flow, present sensor signal sensing technology, sensor data acquisition, and processing technology, as well as sensor fault diagnosis technology based on the voltage, current, speed, and temperature sensors which are commonly used in train traction systems. Finally, intelligent sensors and future research directions of rail transit train sensors are discussed. PMID:28604615

A Mode-Shape-Based Fault Detection Methodology for Cantilever Beams

NASA Technical Reports Server (NTRS)

Tejada, Arturo

2009-01-01

An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for fault detection in critical airframe structures. A particularly promising new technology under development at NASA Langley Research Center is distributed Bragg fiber optic strain sensors. These sensors can be embedded in, for instance, aircraft wings to continuously monitor surface strain during flight. Strain information can then be used in conjunction with well-known vibrational techniques to detect faults due to changes in the wing's physical parameters or to the presence of incipient cracks. To verify the benefits of this technology, the Formal Methods Group at NASA LaRC has proposed the use of formal verification tools such as PVS. The verification process, however, requires knowledge of the physics and mathematics of the vibrational techniques and a clear understanding of the particular fault detection methodology. This report presents a succinct review of the physical principles behind the modeling of vibrating structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of fault detection techniques and proposes a particular detection method for cracks in wings, which is amenable to formal verification. A prototype implementation of these methods using Matlab scripts is also described and is related to the fundamental theoretical concepts.

Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures

NASA Technical Reports Server (NTRS)

Matthews, Bryan L.; Srivastava, Ashok N.

2010-01-01

Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

Nanoscale Roughness of Natural Fault Surfaces Controlled by Scale-Dependent Yield Strength

NASA Astrophysics Data System (ADS)

Thom, C. A.; Brodsky, E. E.; Carpick, R. W.; Pharr, G. M.; Oliver, W. C.; Goldsby, D. L.

2017-09-01

Many natural fault surfaces exhibit remarkably similar scale-dependent roughness, which may reflect the scale-dependent yield strength of rocks. Using atomic force microscopy (AFM), we show that a sample of the Corona Heights Fault exhibits isotropic surface roughness well-described by a power law, with a Hurst exponent of 0.75 +/- 0.05 at all wavelengths from 60 nm to 10 μm. The roughness data and a recently proposed theoretical framework predict that yield strength varies with length scale as λ-0.25+/-0.05. Nanoindentation tests on the Corona Heights sample and another fault sample whose topography was previously measured with AFM (the Yair Fault) reveal a scale-dependent yield stress with power-law exponents of -0.12 +/- 0.06 and -0.18 +/- 0.08, respectively. These values are within one to two standard deviations of the predicted value, and provide experimental evidence that fault roughness is controlled by intrinsic material properties, which produces a characteristic surface geometry.

The Rogowski Coil Sensor in High Current Application: A Review

NASA Astrophysics Data System (ADS)

Nazmy Nanyan, Ayob; Isa, Muzamir; Hamid, Haziah Abdul; Nur Khairul Hafizi Rohani, Mohamad; Ismail, Baharuddin

2018-03-01

Rogowski coil is used for measuring the alternating current (AC) and high-speed current pulses. However, the technology makes the Rogowski coil (RC) come out with more improvement, modification and until today it’s still being studied for the new application. The Rogowski coil has a few advantages compared to the high frequency current transformer (HFCT). A brief review on the basic theory and the application of Rogowski coil as a current sensor measurement that been done by previous researchers are presented and discussed in this paper. Additionally, the review also focused on the capability of Rogowski coil for high current sensor measurement and their application for fault detection, over voltage current sensor, lightning current sensor and high impulse current detection. The experimental set up, techniques and measurement parameters in models also been discussed. Finally, a brief review on the performance analysis of current sensor measurement of Rogowski coil likes sensitivity, the maximum and current detection which could be used as a guideline to another researcher in order to develop an advanced RC as high current sensor in future is presented. This review reveal that the RC has a very good performance in high current sensor detection in term of sensitivity which is up to a few nanosecond, higher bandwidth, excellent in detection of high fault and also could measuring lightning current up to 400kA and has many advantages compare to conventional current transformer(CT).

Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors.

PubMed

Zhang, Chongxing; Dong, Ming; Ren, Ming; Huang, Wenguang; Zhou, Jierui; Gao, Xuze; Albarracín, Ricardo

2018-02-11

Metal-enclosed switchgear, which are widely used in the distribution of electrical energy, play an important role in power distribution networks. Their safe operation is directly related to the reliability of power system as well as the power quality on the consumer side. Partial discharge detection is an effective way to identify potential faults and can be utilized for insulation diagnosis of metal-enclosed switchgear. The transient earth voltage method, an effective non-intrusive method, has substantial engineering application value for estimating the insulation condition of switchgear. However, the practical application effectiveness of TEV detection is not satisfactory because of the lack of a TEV detection application method, i.e., a method with sufficient technical cognition and analysis. This paper proposes an innovative online PD detection system and a corresponding application strategy based on an intelligent feedback distributed TEV wireless sensor network, consisting of sensing, communication, and diagnosis layers. In the proposed system, the TEV signal or status data are wirelessly transmitted to the terminal following low-energy signal preprocessing and acquisition by TEV sensors. Then, a central server analyzes the correlation of the uploaded data and gives a fault warning level according to the quantity, trend, parallel analysis, and phase resolved partial discharge pattern recognition. In this way, a TEV detection system and strategy with distributed acquisition, unitized fault warning, and centralized diagnosis is realized. The proposed system has positive significance for reducing the fault rate of medium voltage switchgear and improving its operation and maintenance level.

Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

NASA Astrophysics Data System (ADS)

Kim, D.; Youn, J.; Kim, C.

2017-08-01

As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

Sliding Mode Fault Tolerant Control with Adaptive Diagnosis for Aircraft Engines

NASA Astrophysics Data System (ADS)

Xiao, Lingfei; Du, Yanbin; Hu, Jixiang; Jiang, Bin

2018-03-01

In this paper, a novel sliding mode fault tolerant control method is presented for aircraft engine systems with uncertainties and disturbances on the basis of adaptive diagnostic observer. By taking both sensors faults and actuators faults into account, the general model of aircraft engine control systems which is subjected to uncertainties and disturbances, is considered. Then, the corresponding augmented dynamic model is established in order to facilitate the fault diagnosis and fault tolerant controller design. Next, a suitable detection observer is designed to detect the faults effectively. Through creating an adaptive diagnostic observer and based on sliding mode strategy, the sliding mode fault tolerant controller is constructed. Robust stabilization is discussed and the closed-loop system can be stabilized robustly. It is also proven that the adaptive diagnostic observer output errors and the estimations of faults converge to a set exponentially, and the converge rate greater than some value which can be adjusted by choosing designable parameters properly. The simulation on a twin-shaft aircraft engine verifies the applicability of the proposed fault tolerant control method.

History of fault slip and interaction with deltaic depostion from the middle Miocene to the Present - Barataria Fault, coastal Louisiana

NASA Astrophysics Data System (ADS)

McLindon, C.

2017-12-01

The Barataria fault is a major component of the Terrebonne Trough, a structural system of faults and salt domes underlying coastal Louisiana. High-quality 3-D seismic reflection data, industry well logs, micro-paleontological data and published literature on regional depositional patterns are integrated to provide an evolutionary history of the Barataria fault. The fault is a segment within a series of south-dipping normal faults that define the northern boundary of the Terrebonne Trough. The fault segment tips at depth interact with the Lake Washington and Bay de Chene salt domes, which appear to have limited its along-strike length. This study shows that the Barataria fault has exhibited continuous but episodic slip since at least the middle Miocene and through the present. Periods of maximum rates of fault slip are related to periods of maximum rates of sediment accumulation associated with Miocene deltaic deposition. The expansion of interval thickness between biostratigraphic markers in the hanging wall section of the fault relative to the footwall section (expansion index) indicate that rates of subsidence on the footwall during active fault slip were substantially greater than on the footwall. Pliocene-Pleistocene stratigraphic intervals exhibiting lower expansion indexes indicate that the fault remained active, but with a pattern of slower slip rate in which stratigraphic thickening was more limited to the area immediately adjacent to the fault. The Barataria fault defines the modern-day width of Barataria Bay, and also has a surface expression in the coastal marsh indicating that recent episodic slip has been associated with patterns of Holocene deltaic deposition.

Model-Based Fault Tolerant Control

NASA Technical Reports Server (NTRS)

Kumar, Aditya; Viassolo, Daniel

2008-01-01

The Model Based Fault Tolerant Control (MBFTC) task was conducted under the NASA Aviation Safety and Security Program. The goal of MBFTC is to develop and demonstrate real-time strategies to diagnose and accommodate anomalous aircraft engine events such as sensor faults, actuator faults, or turbine gas-path component damage that can lead to in-flight shutdowns, aborted take offs, asymmetric thrust/loss of thrust control, or engine surge/stall events. A suite of model-based fault detection algorithms were developed and evaluated. Based on the performance and maturity of the developed algorithms two approaches were selected for further analysis: (i) multiple-hypothesis testing, and (ii) neural networks; both used residuals from an Extended Kalman Filter to detect the occurrence of the selected faults. A simple fusion algorithm was implemented to combine the results from each algorithm to obtain an overall estimate of the identified fault type and magnitude. The identification of the fault type and magnitude enabled the use of an online fault accommodation strategy to correct for the adverse impact of these faults on engine operability thereby enabling continued engine operation in the presence of these faults. The performance of the fault detection and accommodation algorithm was extensively tested in a simulation environment.

Eddy Current Method for Fatigue Testing

NASA Technical Reports Server (NTRS)

Simpson, John W. (Inventor); Fulton, James P. (Inventor); Wincheski, Russell A. (Inventor); Todhunter, Ronald G. (Inventor); Namkung, Min (Inventor); Nath, Shridhar C. (Inventor)

1997-01-01

Flux-focusing electromagnetic sensor using a ferromagnetic flux-focusing lens simplifies inspections and increases detectability of fatigue cracks and material loss in high conductivity material. A ferrous shield isolates a high-turn pick-up coil from an excitation coil. Use of the magnetic shield produces a null voltage output across the receiving coil in presence of an unflawed sample. Redistribution of the current flow in the sample caused by the presence of flaws. eliminates the shielding condition and a large output voltage is produced, yielding a clear unambiguous flaw signal. Maximum sensor output is obtained when positioned symmetrically above the crack. By obtaining position of maximum sensor output, it is possible to track the fault and locate the area surrounding its tip. Accuracy of tip location is enhanced by two unique features of the sensor; a very high signal-to-noise ratio of the probe's output resulting in an extremely smooth signal peak across the fault, and a rapidly decaying sensor output outside a small area surrounding the crack tip enabling the search region to be clearly defined. Under low frequency operation, material thinning due to corrosion causes incomplete shielding of the pick-up coil. Low frequency output voltage of the probe is therefore a direct indicator of thickness of the test sample. Fatigue testing a conductive material is accomplished by applying load to the material, applying current to the sensor, scanning the material with the sensor, monitoring the sensor output signal, adjusting material load based on the sensor output signal of the sensor, and adjusting position of the sensor based on its output signal.

Delineation of the North Anatolian Fault Within the Sapanca Lake and Correlation of Seismo-Turbidites With Major Earthquakes

NASA Astrophysics Data System (ADS)

Gulen, L.; Demirbağ, E.; Cagatay, M. N.; Yıldırım, E.; Yalamaz, B.

2015-12-01

Seismic reflection studies have been carried out in the Sapanca Lake to delineate the geometry of the North Anatolian Fault. A total of 28 N-S and 2 E-W trending seismic profiles were obtained. The interpretation of seismic reflection profiles have revealed that the North Anatolian Fault Zone exhibits a pull-apart fault geometry within the Sapanca Lake and the active fault segments have been mapped. A bathymetry map of the Sapanca Lake is also generated and the maximum depth is determined to be 54 m. A systematic study of the sedimentological, physical and geochemical properties of three up to 75.7 cm long water-sediment interface cores located along depth transects ranging from 43 to 5.1.5 m water depth. The cores were analyzed using Geotek Multi Sensor Core Logger (MSCL) for physical properties, laser particle size analyzer for granulometry, TOC Analyzer for Total Organic Organic (TOC) and Total Inorganic carbon (TIC) analysis and Itrax-XRF Core Scanner for elemental analysis and digital X-RAY Radiography. The Sapanca Lake earthquake records are characterized by seismo-turbidites consisting of grey or dark grey coarse to fine sand and silty mud with a sharp basal and transitional upper boundaries. The units commonly show normal size grading with their basal parts showing high density and magnetic susceptibility and enrichment in one or more of elements, such as Si, Ca, Tİ, K, Rb, Zr and Fe, indicative of coarse detrial input. Based on radionuclide and radiocarbon analyses the seismo-turbidites are correlated with the 1999 İzmit and Düzce (Mw=7.4 and 7.2), 1967 Mudurnu (Mw= 6.8), and 1957 Abant (Mw= 7.1) Earthquakes. Additionally a prominent Cs137 peak was found in the Sapanca Lake sediment cores at a depth of 12 cm. indicating that a radioactive fallout occurred in the region as a result of the 1986 Chernobyl Nuclear Power Plant accident in Ukraine.

DEVELOPMENT AND TESTING OF FAULT-DIAGNOSIS ALGORITHMS FOR REACTOR PLANT SYSTEMS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grelle, Austin L.; Park, Young S.; Vilim, Richard B.

Argonne National Laboratory is further developing fault diagnosis algorithms for use by the operator of a nuclear plant to aid in improved monitoring of overall plant condition and performance. The objective is better management of plant upsets through more timely, informed decisions on control actions with the ultimate goal of improved plant safety, production, and cost management. Integration of these algorithms with visual aids for operators is taking place through a collaboration under the concept of an operator advisory system. This is a software entity whose purpose is to manage and distill the enormous amount of information an operator mustmore » process to understand the plant state, particularly in off-normal situations, and how the state trajectory will unfold in time. The fault diagnosis algorithms were exhaustively tested using computer simulations of twenty different faults introduced into the chemical and volume control system (CVCS) of a pressurized water reactor (PWR). The algorithms are unique in that each new application to a facility requires providing only the piping and instrumentation diagram (PID) and no other plant-specific information; a subject-matter expert is not needed to install and maintain each instance of an application. The testing approach followed accepted procedures for verifying and validating software. It was shown that the code satisfies its functional requirement which is to accept sensor information, identify process variable trends based on this sensor information, and then to return an accurate diagnosis based on chains of rules related to these trends. The validation and verification exercise made use of GPASS, a one-dimensional systems code, for simulating CVCS operation. Plant components were failed and the code generated the resulting plant response. Parametric studies with respect to the severity of the fault, the richness of the plant sensor set, and the accuracy of sensors were performed as part of the validation exercise. The background and overview of the software will be presented to give an overview of the approach. Following, the verification and validation effort using the GPASS code for simulation of plant transients including a sensitivity study on important parameters will be presented« less

LC Circuits for Diagnosing Embedded Piezoelectric Devices

NASA Technical Reports Server (NTRS)

Chattin, Richard L.; Fox, Robert Lee; Moses, Robert W.; Shams, Qamar A.

2005-01-01

A recently invented method of nonintrusively detecting faults in piezoelectric devices involves measurement of the resonance frequencies of inductor capacitor (LC) resonant circuits. The method is intended especially to enable diagnosis of piezoelectric sensors, actuators, and sensor/actuators that are embedded in structures and/or are components of multilayer composite material structures.

The Virtual Quake earthquake simulator: a simulation-based forecast of the El Mayor-Cucapah region and evidence of predictability in simulated earthquake sequences

NASA Astrophysics Data System (ADS)

Yoder, Mark R.; Schultz, Kasey W.; Heien, Eric M.; Rundle, John B.; Turcotte, Donald L.; Parker, Jay W.; Donnellan, Andrea

2015-12-01

In this manuscript, we introduce a framework for developing earthquake forecasts using Virtual Quake (VQ), the generalized successor to the perhaps better known Virtual California (VC) earthquake simulator. We discuss the basic merits and mechanics of the simulator, and we present several statistics of interest for earthquake forecasting. We also show that, though the system as a whole (in aggregate) behaves quite randomly, (simulated) earthquake sequences limited to specific fault sections exhibit measurable predictability in the form of increasing seismicity precursory to large m > 7 earthquakes. In order to quantify this, we develop an alert-based forecasting metric, and show that it exhibits significant information gain compared to random forecasts. We also discuss the long-standing question of activation versus quiescent type earthquake triggering. We show that VQ exhibits both behaviours separately for independent fault sections; some fault sections exhibit activation type triggering, while others are better characterized by quiescent type triggering. We discuss these aspects of VQ specifically with respect to faults in the Salton Basin and near the El Mayor-Cucapah region in southern California, USA and northern Baja California Norte, Mexico.

The Virtual Quake Earthquake Simulator: Earthquake Probability Statistics for the El Mayor-Cucapah Region and Evidence of Predictability in Simulated Earthquake Sequences

NASA Astrophysics Data System (ADS)

Schultz, K.; Yoder, M. R.; Heien, E. M.; Rundle, J. B.; Turcotte, D. L.; Parker, J. W.; Donnellan, A.

2015-12-01

We introduce a framework for developing earthquake forecasts using Virtual Quake (VQ), the generalized successor to the perhaps better known Virtual California (VC) earthquake simulator. We discuss the basic merits and mechanics of the simulator, and we present several statistics of interest for earthquake forecasting. We also show that, though the system as a whole (in aggregate) behaves quite randomly, (simulated) earthquake sequences limited to specific fault sections exhibit measurable predictability in the form of increasing seismicity precursory to large m > 7 earthquakes. In order to quantify this, we develop an alert based forecasting metric similar to those presented in Keilis-Borok (2002); Molchan (1997), and show that it exhibits significant information gain compared to random forecasts. We also discuss the long standing question of activation vs quiescent type earthquake triggering. We show that VQ exhibits both behaviors separately for independent fault sections; some fault sections exhibit activation type triggering, while others are better characterized by quiescent type triggering. We discuss these aspects of VQ specifically with respect to faults in the Salton Basin and near the El Mayor-Cucapah region in southern California USA and northern Baja California Norte, Mexico.

Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

NASA Technical Reports Server (NTRS)

Narasimhan, Sriram; Mengshoel, Ole

2008-01-01

Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

Intelligent Control and Health Monitoring. Chapter 3

NASA Technical Reports Server (NTRS)

Garg, Sanjay; Kumar, Aditya; Mathews, H. Kirk; Rosenfeld, Taylor; Rybarik, Pavol; Viassolo, Daniel E.

2009-01-01

Advanced model-based control architecture overcomes the limitations state-of-the-art engine control and provides the potential of virtual sensors, for example for thrust and stall margin. "Tracking filters" are used to adapt the control parameters to actual conditions and to individual engines. For health monitoring standalone monitoring units will be used for on-board analysis to determine the general engine health and detect and isolate sudden faults. Adaptive models open up the possibility of adapting the control logic to maintain desired performance in the presence of engine degradation or to accommodate any faults. Improved and new sensors are required to allow sensing at stations within the engine gas path that are currently not instrumented due in part to the harsh conditions including high operating temperatures and to allow additional monitoring of vibration, mass flows and energy properties, exhaust gas composition, and gas path debris. The environmental and performance requirements for these sensors are summarized.

Instrument and spacecraft faults associated with nuclear radiation in space

NASA Technical Reports Server (NTRS)

Trainos, J. H.

1994-01-01

A review is given which surveys the variety of faults and failures which have occurred in space due both to the effects of single, energetic nuclear particles, as well as effects due to the accumulated ionizing dose or the fluence of nuclear particles. The review covers a variety of problems with sensors, electronics, instruments and spacecraft from several countries.

Fault-Tolerant Algorithms for Connectivity Restoration in Wireless Sensor Networks.

PubMed

Zeng, Yali; Xu, Li; Chen, Zhide

2015-12-22

As wireless sensor network (WSN) is often deployed in a hostile environment, nodes in the networks are prone to large-scale failures, resulting in the network not working normally. In this case, an effective restoration scheme is needed to restore the faulty network timely. Most of existing restoration schemes consider more about the number of deployed nodes or fault tolerance alone, but fail to take into account the fact that network coverage and topology quality are also important to a network. To address this issue, we present two algorithms named Full 2-Connectivity Restoration Algorithm (F2CRA) and Partial 3-Connectivity Restoration Algorithm (P3CRA), which restore a faulty WSN in different aspects. F2CRA constructs the fan-shaped topology structure to reduce the number of deployed nodes, while P3CRA constructs the dual-ring topology structure to improve the fault tolerance of the network. F2CRA is suitable when the restoration cost is given the priority, and P3CRA is suitable when the network quality is considered first. Compared with other algorithms, these two algorithms ensure that the network has stronger fault-tolerant function, larger coverage area and better balanced load after the restoration.

Sensor Data Quality and Angular Rate Down-Selection Algorithms on SLS EM-1

NASA Technical Reports Server (NTRS)

Park, Thomas; Oliver, Emerson; Smith, Austin

2018-01-01

The NASA Space Launch System Block 1 launch vehicle is equipped with an Inertial Navigation System (INS) and multiple Rate Gyro Assemblies (RGA) that are used in the Guidance, Navigation, and Control (GN&C) algorithms. The INS provides the inertial position, velocity, and attitude of the vehicle along with both angular rate and specific force measurements. Additionally, multiple sets of co-located rate gyros supply angular rate data. The collection of angular rate data, taken along the launch vehicle, is used to separate out vehicle motion from flexible body dynamics. Since the system architecture uses redundant sensors, the capability was developed to evaluate the health (or validity) of the independent measurements. A suite of Sensor Data Quality (SDQ) algorithms is responsible for assessing the angular rate data from the redundant sensors. When failures are detected, SDQ will take the appropriate action and disqualify or remove faulted sensors from forward processing. Additionally, the SDQ algorithms contain logic for down-selecting the angular rate data used by the GN&C software from the set of healthy measurements. This paper provides an overview of the algorithms used for both fault-detection and measurement down selection.

Research of diagnosis sensors fault based on correlation analysis of the bridge structural health monitoring system

NASA Astrophysics Data System (ADS)

Hu, Shunren; Chen, Weimin; Liu, Lin; Gao, Xiaoxia

2010-03-01

Bridge structural health monitoring system is a typical multi-sensor measurement system due to the multi-parameters of bridge structure collected from the monitoring sites on the river-spanning bridges. Bridge structure monitored by multi-sensors is an entity, when subjected to external action; there will be different performances to different bridge structure parameters. Therefore, the data acquired by each sensor should exist countless correlation relation. However, complexity of the correlation relation is decided by complexity of bridge structure. Traditionally correlation analysis among monitoring sites is mainly considered from physical locations. unfortunately, this method is so simple that it cannot describe the correlation in detail. The paper analyzes the correlation among the bridge monitoring sites according to the bridge structural data, defines the correlation of bridge monitoring sites and describes its several forms, then integrating the correlative theory of data mining and signal system to establish the correlation model to describe the correlation among the bridge monitoring sites quantificationally. Finally, The Chongqing Mashangxi Yangtze river bridge health measurement system is regards as research object to diagnosis sensors fault, and simulation results verify the effectiveness of the designed method and theoretical discussions.

Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

PubMed Central

Li, Chao; Zhang, Zhenjiang; Chao, Han-Chieh

2017-01-01

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance. PMID:29280950

FINDS: A fault inferring nonlinear detection system programmers manual, version 3.0

NASA Technical Reports Server (NTRS)

Lancraft, R. E.

1985-01-01

Detailed software documentation of the digital computer program FINDS (Fault Inferring Nonlinear Detection System) Version 3.0 is provided. FINDS is a highly modular and extensible computer program designed to monitor and detect sensor failures, while at the same time providing reliable state estimates. In this version of the program the FINDS methodology is used to detect, isolate, and compensate for failures in simulated avionics sensors used by the Advanced Transport Operating Systems (ATOPS) Transport System Research Vehicle (TSRV) in a Microwave Landing System (MLS) environment. It is intended that this report serve as a programmers guide to aid in the maintenance, modification, and revision of the FINDS software.

Design of LPV fault-tolerant controller for pitch system of wind turbine

NASA Astrophysics Data System (ADS)

Wu, Dinghui; Zhang, Xiaolin

2017-07-01

To address failures of wind turbine pitch-angle sensors, traditional wind turbine linear parameter varying (LPV) model is transformed into a double-layer convex polyhedron LPV model. On the basis of this model, when the plurality of the sensor undergoes failure and details of the failure are inconvenient to obtain, each sub-controller is designed using distributed thought and gain scheduling method. The final controller is obtained using all of the sub-controllers by a convex combination. The design method corrects the errors of the linear model, improves the linear degree of the system, and solves the problem of multiple pitch angle faults to ensure stable operation of the wind turbine.

Fault Detection and Safety in Closed-Loop Artificial Pancreas Systems

PubMed Central

2014-01-01

Continuous subcutaneous insulin infusion pumps and continuous glucose monitors enable individuals with type 1 diabetes to achieve tighter blood glucose control and are critical components in a closed-loop artificial pancreas. Insulin infusion sets can fail and continuous glucose monitor sensor signals can suffer from a variety of anomalies, including signal dropout and pressure-induced sensor attenuations. In addition to hardware-based failures, software and human-induced errors can cause safety-related problems. Techniques for fault detection, safety analyses, and remote monitoring techniques that have been applied in other industries and applications, such as chemical process plants and commercial aircraft, are discussed and placed in the context of a closed-loop artificial pancreas. PMID:25049365

Development and evaluation of virtual refrigerant mass flow sensors for fault detection and diagnostics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Woohyun; Braun, J.

Refrigerant mass flow rate is an important measurement for monitoring equipment performance and enabling fault detection and diagnostics. However, a traditional mass flow meter is expensive to purchase and install. A virtual refrigerant mass flow sensor (VRMF) uses a mathematical model to estimate flow rate using low-cost measurements and can potentially be implemented at low cost. This study evaluates three VRMFs for estimating refrigerant mass flow rate. The first model uses a compressor map that relates refrigerant flow rate to measurements of inlet and outlet pressure, and inlet temperature measurements. The second model uses an energy-balance method on the compressormore » that uses a compressor map for power consumption, which is relatively independent of compressor faults that influence mass flow rate. The third model is developed using an empirical correlation for an electronic expansion valve (EEV) based on an orifice equation. The three VRMFs are shown to work well in estimating refrigerant mass flow rate for various systems under fault-free conditions with less than 5% RMS error. Each of the three mass flow rate estimates can be utilized to diagnose and track the following faults: 1) loss of compressor performance, 2) fouled condenser or evaporator filter, 3) faulty expansion device, respectively. For example, a compressor refrigerant flow map model only provides an accurate estimation when the compressor operates normally. When a compressor is not delivering the expected flow due to a leaky suction or discharge valve or other internal fault, the energy-balance or EEV model can provide accurate flow estimates. In this paper, the flow differences provide an indication of loss of compressor performance and can be used for fault detection and diagnostics.« less

Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach

PubMed Central

2017-01-01

Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system. PMID:28946673

Microstructures and rheology of a calcite-shale thrust fault

NASA Astrophysics Data System (ADS)

Wells, Rachel K.; Newman, Julie; Wojtal, Steven

2014-08-01

A thin (˜2 cm) layer of extensively sheared fault rock decorates the ˜15 km displacement Copper Creek thrust at an exposure near Knoxville, TN (USA). In these ultrafine-grained (<0.3 μm) fault rocks, interpenetrating calcite grains form an interconnected network around shale clasts. One cm below the fault rock layer, sedimentary laminations in non-penetratively deformed footwall shale are cut by calcite veins, small faults, and stylolites. A 350 μm thick calcite vein separates the fault rocks and footwall shale. The vein is composed of layers of (1) coarse calcite grains (>5 μm) that exhibit a lattice preferred orientation (LPO) with pores at twin-twin and twin-grain boundary intersections, and (2) ultrafine-grained (0.3 μm) calcite that exhibits interpenetrating grain boundaries, four-grain junctions and lacks a LPO. Coarse calcite layers crosscut ultrafine-grained layers indicating intermittent vein formation during shearing. Calcite in the fault rock layer is derived from vein calcite and grain-size reduction of calcite took place by plasticity-induced fracture. The ultrafine-grained calcite deformed primarily by diffusion-accommodated grain boundary sliding and formed an interconnected network around shale clasts within the shear zone. The interconnected network of ultrafine-grained calcite indicates that calcite, not shale, was the weak phase in this fault zone.

Fault detection and diagnosis for refrigerator from compressor sensor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keres, Stephen L.; Gomes, Alberto Regio; Litch, Andrew D.

A refrigerator, a sealed refrigerant system, and method are provided where the refrigerator includes at least a refrigerated compartment and a sealed refrigerant system including an evaporator, a compressor, a condenser, a controller, an evaporator fan, and a condenser fan. The method includes monitoring a frequency of the compressor, and identifying a fault condition in the at least one component of the refrigerant sealed system in response to the compressor frequency. The method may further comprise calculating a compressor frequency rate based upon the rate of change of the compressor frequency, wherein a fault in the condenser fan is identifiedmore » if the compressor frequency rate is positive and exceeds a condenser fan fault threshold rate, and wherein a fault in the evaporator fan is identified if the compressor frequency rate is negative and exceeds an evaporator fan fault threshold rate.« less

Sensor Data Quality and Angular Rate Down-Selection Algorithms on SLS EM-1

NASA Technical Reports Server (NTRS)

Park, Thomas; Smith, Austin; Oliver, T. Emerson

2018-01-01

The NASA Space Launch System Block 1 launch vehicle is equipped with an Inertial Navigation System (INS) and multiple Rate Gyro Assemblies (RGA) that are used in the Guidance, Navigation, and Control (GN&C) algorithms. The INS provides the inertial position, velocity, and attitude of the vehicle along with both angular rate and specific force measurements. Additionally, multiple sets of co-located rate gyros supply angular rate data. The collection of angular rate data, taken along the launch vehicle, is used to separate out vehicle motion from flexible body dynamics. Since the system architecture uses redundant sensors, the capability was developed to evaluate the health (or validity) of the independent measurements. A suite of Sensor Data Quality (SDQ) algorithms is responsible for assessing the angular rate data from the redundant sensors. When failures are detected, SDQ will take the appropriate action and disqualify or remove faulted sensors from forward processing. Additionally, the SDQ algorithms contain logic for down-selecting the angular rate data used by the GNC software from the set of healthy measurements. This paper explores the trades and analyses that were performed in selecting a set of robust fault-detection algorithms included in the GN&C flight software. These trades included both an assessment of hardware-provided health and status data as well as an evaluation of different algorithms based on time-to-detection, type of failures detected, and probability of detecting false positives. We then provide an overview of the algorithms used for both fault-detection and measurement down selection. We next discuss the role of trajectory design, flexible-body models, and vehicle response to off-nominal conditions in setting the detection thresholds. Lastly, we present lessons learned from software integration and hardware-in-the-loop testing.

NanTroSEIZE observatories: Installation of a long-term borehole monitoring systems offshore the Kii Peninsula, Japan

NASA Astrophysics Data System (ADS)

Kopf, A.; Saffer, D. M.; Davis, E. E.; Araki, E.; Kinoshita, M.; Lauer, R. M.; Wheat, C. G.; Kitada, K.; Kimura, T.; Toczko, S.; Eguchi, N. O.; Science Parties, E.

2010-12-01

The IODP Nankai Trough Seismogenic Zone Experiment (NanTroSEIZE) is a multi-expedition drilling program designed to investigate fault mechanics, fault slip behavior, and strain accumulation along subduction megathrusts, through coring, logging, and long-term monitoring experiments. One key objective is the development and installation of a borehole observatory network extending from locations above the outer, presumably aseismic accretionary wedge to the seismogenic and interseismically locked plate interface, to record seismicity and slip transients, monitor strain accumulation, document hydraulic transients associated with deformation events, and quantify in situ pore fluid pressure and temperature. As part of recent NanTroSEIZE operations, borehole instruments have been developed for deployment at two sites: (1) Site C0010, which penetrates a major out-of-sequence thrust fault termed the “megasplay” at ca. 400 mbsf, and (2) Site C0002 in the Kumano forearc basin at a location that overlies both the updip edge of the inferred interseismically locked portion of the plate interface, and clusters of very low frequency thrust and reverse earthquakes located within the accretionary prism and potentially on the megasplay fault. In 2009, Site C0010 was drilled and cased with screens to access the megasplay fault, and a simple pore pressure and temperature monitoring system (a ”smartplug”) was installed. The simple observatory unit includes pressure and temperature sensors and a data logging package mounted beneath a mechanically set retrievable casing packer, and includes two pressure sensors, one in hydraulic communication with the formation through the casing screens below the packer, and the other to the open borehole above the packer to record hydrostatic reference pressure and ocean loading signals. Temperatures are recorded within the instrument package using a platinum thermometer and by a self-contained miniature temperature logger (MTL). In fall 2010, the smartplug will be retrieved and replaced with an upgraded instrument package that also includes an autonomous osmotic geochemical sampling system and microbial colonization experiment. Fall 2010 operations will also drill and case Site C0002 to ca. 1000 m depth and install a newly developed multi-sensor permanent observatory system, which includes a volumetric strainmeter, a broadband seismometer, tiltmeter, thermister string, and multi-level pore-pressure sensors. The strain, seismometer, and tilt sensors will be cemented with the basal mudstones of the Kumano basin, and pore pressure will be monitored within both the underlying accretionary prism and within the lower basin sediments. The observatory will ultimately be connected to the seafloor fiber-optic cable network DONET. Here, we report on the retrieval of the smartplug, installation and configuration of the new multi-sensor permanent observatory, and preliminary data obtained from the smartplug deployment.

Sequential behavior and its inherent tolerance to memory faults.

NASA Technical Reports Server (NTRS)

Meyer, J. F.

1972-01-01

Representation of a memory fault of a sequential machine M by a function mu on the states of M and the result of the fault by an appropriately determined machine M(mu). Given some sequential behavior B, its inherent tolerance to memory faults can then be measured in terms of the minimum memory redundancy required to realize B with a state-assigned machine having fault tolerance type tau and fault tolerance level t. A behavior having maximum inherent tolerance is exhibited, and it is shown that behaviors of the same size can have different inherent tolerance.

Meeting the Challenges of Exploration Systems: Health Management Technologies for Aerospace Systems With Emphasis on Propulsion

NASA Technical Reports Server (NTRS)

Melcher, Kevin J.; Sowers, T. Shane; Maul, William A.

2005-01-01

The constraints of future Exploration Missions will require unique Integrated System Health Management (ISHM) capabilities throughout the mission. An ambitious launch schedule, human-rating requirements, long quiescent periods, limited human access for repair or replacement, and long communication delays all require an ISHM system that can span distinct yet interdependent vehicle subsystems, anticipate failure states, provide autonomous remediation, and support the Exploration Mission from beginning to end. NASA Glenn Research Center has developed and applied health management system technologies to aerospace propulsion systems for almost two decades. Lessons learned from past activities help define the approach to proper ISHM development: sensor selection- identifies sensor sets required for accurate health assessment; data qualification and validation-ensures the integrity of measurement data from sensor to data system; fault detection and isolation-uses measurements in a component/subsystem context to detect faults and identify their point of origin; information fusion and diagnostic decision criteria-aligns data from similar and disparate sources in time and use that data to perform higher-level system diagnosis; and verification and validation-uses data, real or simulated, to provide variable exposure to the diagnostic system for faults that may only manifest themselves in actual implementation, as well as faults that are detectable via hardware testing. This presentation describes a framework for developing health management systems and highlights the health management research activities performed by the Controls and Dynamics Branch at the NASA Glenn Research Center. It illustrates how those activities contribute to the development of solutions for Integrated System Health Management.

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

Smart sensor technology for advanced launch vehicles

NASA Astrophysics Data System (ADS)

Schoess, Jeff

1989-07-01

Next-generation advanced launch vehicles will require improved use of sensor data and the management of multisensor resources to achieve automated preflight checkout, prelaunch readiness assessment and vehicle inflight condition monitoring. Smart sensor technology is a key component in meeting these needs. This paper describes the development of a smart sensor-based condition monitoring system concept referred to as the Distributed Sensor Architecture. A significant event and anomaly detection scheme that provides real-time condition assessment and fault diagnosis of advanced launch system rocket engines is described. The design and flight test of a smart autonomous sensor for Space Shuttle structural integrity health monitoring is presented.

Using Neural Networks for Sensor Validation

NASA Technical Reports Server (NTRS)

Mattern, Duane L.; Jaw, Link C.; Guo, Ten-Huei; Graham, Ronald; McCoy, William

1998-01-01

This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a model-based approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component analysis on a set of redundant sensors to provide an estimate for a single failed sensor. The approaches are demonstrated using a nonlinear simulation of a turbofan engine. The fault detection and sensor estimation results are presented and the training of the auto-associative neural network to provide sensor estimates is discussed.

A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition.

PubMed

Zhang, Xiaorong; Huang, He

2015-02-19

Unreliability of surface EMG recordings over time is a challenge for applying the EMG pattern recognition (PR)-controlled prostheses in clinical practice. Our previous study proposed a sensor fault-tolerant module (SFTM) by utilizing redundant information in multiple EMG signals. The SFTM consists of multiple sensor fault detectors and a self-recovery mechanism that can identify anomaly in EMG signals and remove the recordings of the disturbed signals from the input of the pattern classifier to recover the PR performance. While the proposed SFTM has shown great promise, the previous design is impractical. A practical SFTM has to be fast enough, lightweight, automatic, and robust under different conditions with or without disturbances. This paper presented a real-time, practical SFTM towards robust EMG PR. A novel fast LDA retraining algorithm and a fully automatic sensor fault detector based on outlier detection were developed, which allowed the SFTM to promptly detect disturbances and recover the PR performance immediately. These components of SFTM were then integrated with the EMG PR module and tested on five able-bodied subjects and a transradial amputee in real-time for classifying multiple hand and wrist motions under different conditions with different disturbance types and levels. The proposed fast LDA retraining algorithm significantly shortened the retraining time from nearly 1 s to less than 4 ms when tested on the embedded system prototype, which demonstrated the feasibility of a nearly "zero-delay" SFTM that is imperceptible to the users. The results of the real-time tests suggested that the SFTM was able to handle different types of disturbances investigated in this study and significantly improve the classification performance when one or multiple EMG signals were disturbed. In addition, the SFTM could also maintain the system's classification performance when there was no disturbance. This paper presented a real-time, lightweight, and automatic SFTM, which paved the way for reliable and robust EMG PR for prosthesis control.

A dynamic fault tree model of a propulsion system

NASA Technical Reports Server (NTRS)

Xu, Hong; Dugan, Joanne Bechta; Meshkat, Leila

2006-01-01

We present a dynamic fault tree model of the benchmark propulsion system, and solve it using Galileo. Dynamic fault trees (DFT) extend traditional static fault trees with special gates to model spares and other sequence dependencies. Galileo solves DFT models using a judicious combination of automatically generated Markov and Binary Decision Diagram models. Galileo easily handles the complexities exhibited by the benchmark problem. In particular, Galileo is designed to model phased mission systems.

[Advanced Development for Space Robotics With Emphasis on Fault Tolerance Technology

NASA Technical Reports Server (NTRS)

Tesar, Delbert

1997-01-01

This report describes work developing fault tolerant redundant robotic architectures and adaptive control strategies for robotic manipulator systems which can dynamically accommodate drastic robot manipulator mechanism, sensor or control failures and maintain stable end-point trajectory control with minimum disturbance. Kinematic designs of redundant, modular, reconfigurable arms for fault tolerance were pursued at a fundamental level. The approach developed robotic testbeds to evaluate disturbance responses of fault tolerant concepts in robotic mechanisms and controllers. The development was implemented in various fault tolerant mechanism testbeds including duality in the joint servo motor modules, parallel and serial structural architectures, and dual arms. All have real-time adaptive controller technologies to react to mechanism or controller disturbances (failures) to perform real-time reconfiguration to continue the task operations. The developments fall into three main areas: hardware, software, and theoretical.

Long Term Observations of Subsurface Pore Pressure in the Kumano Basin and Upper Accretionary Wedge along the NanTroSIEZE Transect, offshore Japan: Signals from the 2011 Tohoku Earthquake

NASA Astrophysics Data System (ADS)

Zhang, Y.; Saffer, D. M.

2013-12-01

Subsurface pore pressure as a sensitive measure of strain and formation properties has provided insights into the wide range of fault slip behaviors, contributing to the understanding of fault and earthquake mechanics. Pore pressures from off shore borehole observatory are especially important, as 1) they are the only detectable signals of small and slow events; 2) they provide our only access to the outer forearc, where the tsunami hazards are triggered by the fault slip. As part of the Nankai Trough Seismogenic Zone Experiment (NanTroSEIZE) a suite of borehole sensors were installed as part of a long-term borehole observatory at IODP Site C0002, during IODP Expedition # 332 in December of 2010. The observatory includes a broadband seismometer, short period geophones, a volumetric strainmeter, temperature sensors, an accelerometer, and formation pore pressure monitoring at two depths: one in the mudstones of the Kumano Basin in an interval spanning 757-780 meters below seafloor (mbsf), and a second in the uppermost accretionary wedge in an interval from 937 - 980 mbsf. Here, we report on pore pressure records acquired at a sampling frequency of 1/60 Hz, spanning the period from December 2010 to January 2013, which were recovered in early 2013. We observe a clear hydraulic signal from March 11, 2011 Tohoku earthquake and aftershocks, including both dynamic pore pressure changes during passage of surface waves and shifts in formation pressure following the event. Pressure exhibit an increase of ~3 kPa in the upper sediment screened interval following the earthquake, and decrease by ~5 kPa in the accretionary prism interval. Both of the offset changes persist through the end of the data recording. These pore pressure changes may reflect static stress changes from the earthquake, or local site effects related to shaking. We also observe a clear increase in formation pore pressures associated with drilling operations at nearby holes in November and December 2012. These inadvertent two-well tests provide information about formation hydraulic properties at the ~20-50 m scale.

ROBUST ONLINE MONITORING FOR CALIBRATION ASSESSMENT OF TRANSMITTERS AND INSTRUMENTATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramuhalli, Pradeep; Tipireddy, Ramakrishna; Lerchen, Megan E.

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. Specifically, the next generation of OLM technology is expected to include newly developed advanced algorithms that improve monitoring of sensor/system performance and enable the use of plant data to derive information that currently cannot be measured. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this paper, we discuss an overview of research beingmore » performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or more sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation – fault detection and selection of acceptance criteria • Virtual sensing – signal value prediction and acceptance criteria • Response-time assessment – fault detection and acceptance criteria selection A GP-based uncertainty quantification (UQ) method previously developed for UQ in OLM, was adapted for use in sensor-fault detection and virtual sensing. For signal validation, the various components to the OLM residual (which is computed using an AAKR model) were explicitly defined and modeled using a GP. Evaluation was conducted using flow loop data from multiple sources. Results using experimental data from laboratory-scale flow loops indicate that the approach, while capable of detecting sensor drift, may be incapable of discriminating between sensor drift and model inadequacy. This may be due to a simplification applied in the initial modeling, where the sensor degradation is assumed to be stationary. In the case of virtual sensors, the GP model was used in a predictive mode to estimate the correct sensor reading for sensors that may have failed. Results have indicated the viability of using this approach for virtual sensing. However, the GP model has proven to be computationally expensive, and so alternative algorithms for virtual sensing are being evaluated. Finally, automated approaches to performing noise analysis for extracting sensor response time were developed. Evaluation of this technique using laboratory-scale data indicates that it compares well with manual techniques previously used for noise analysis. Moreover, the automated and manual approaches for noise analysis also compare well with the current “gold standard”, hydraulic ramp testing, for response time monitoring. Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.« less

Breaking down barriers in cooperative fault management: Temporal and functional information displays

NASA Technical Reports Server (NTRS)

Potter, Scott S.; Woods, David D.

1994-01-01

At the highest level, the fundamental question addressed by this research is how to aid human operators engaged in dynamic fault management. In dynamic fault management there is some underlying dynamic process (an engineered or physiological process referred to as the monitored process - MP) whose state changes over time and whose behavior must be monitored and controlled. In these types of applications (dynamic, real-time systems), a vast array of sensor data is available to provide information on the state of the MP. Faults disturb the MP and diagnosis must be performed in parallel with responses to maintain process integrity and to correct the underlying problem. These situations frequently involve time pressure, multiple interacting goals, high consequences of failure, and multiple interleaved tasks.

Pulse based sensor networking using mechanical waves through metal substrates

NASA Astrophysics Data System (ADS)

Lorenz, S.; Dong, B.; Huo, Q.; Tomlinson, W. J.; Biswas, S.

2013-05-01

This paper presents a novel wireless sensor networking technique using ultrasonic signal as the carrier wave for binary data exchange. Using the properties of lamb wave propagation through metal substrates, the proposed network structure can be used for runtime transport of structural fault information to ultrasound access points. Primary applications of the proposed sensor networking technique will include conveying fault information on an aircraft wing or on a bridge to an ultrasonic access point using ultrasonic wave through the structure itself (i.e. wing or bridge). Once a fault event has been detected, a mechanical pulse is forwarded to the access node using shortest path multi-hop ultrasonic pulse routing. The advantages of mechanical waves over traditional radio transmission using pulses are the following: First, unlike radio frequency, surface acoustic waves are not detectable outside the medium, which increases the inherent security for sensitive environments in respect to tapping. Second, event detection can be represented by the injection of a single mechanical pulse at a specific temporal position, whereas radio messages usually take several bits. The contributions of this paper are: 1) Development of a transceiver for transmitting/receiving ultrasound pulses with a pulse loss rate below 2·10-5 and false positive rate with an upper bound of 2·10-4. 2) A novel one-hop distance estimation based on the properties of lamb wave propagation with an accuracy of above 80%. 3) Implementation of a wireless sensor network using mechanical wave propagation for event detection on a 2024 aluminum alloy commonly used for aircraft skin construction.

Investigation of Gas-Sensing Property of Acid-Deposited Polyaniline Thin-Film Sensors for Detecting H2S and SO2

PubMed Central

Dong, Xingchen; Zhang, Xiaoxing; Wu, Xiaoqing; Cui, Hao; Chen, Dachang

2016-01-01

Latent insulation defects introduced in manufacturing process of gas-insulated switchgears can lead to partial discharge during long-time operation, even to insulation fault if partial discharge develops further. Monitoring of decomposed components of SF6, insulating medium of gas-insulated switchgear, is a feasible method of early-warning to avoid the occurrence of sudden fault. Polyaniline thin-film with protonic acid deposited possesses wide application prospects in the gas-sensing field. Polyaniline thin-film sensors with only sulfosalicylic acid deposited and with both hydrochloric acid and sulfosalicylic acid deposited were prepared by chemical oxidative polymerization method. Gas-sensing experiment was carried out to test properties of new sensors when exposed to H2S and SO2, two decomposed products of SF6 under discharge. The gas-sensing properties of these two sensors were compared with that of a hydrochloric acid deposited sensor. Results show that the hydrochloric acid and sulfosalicylic acid deposited polyaniline thin-film sensor shows the most outstanding sensitivity and selectivity to H2S and SO2 when concentration of gases range from 10 to 100 μL/L, with sensitivity changing linearly with concentration of gases. The sensor also possesses excellent long-time and thermal stability. This research lays the foundation for preparing practical gas-sensing devices to detect H2S and SO2 in gas-insulated switchgears at room temperature. PMID:27834895

Foreshocks and Aftershocks Detected from Stick-slip Events on a 3 m Biaxial Apparatus and their Relationship to Quasistatic Nucleation and Wear Processes

NASA Astrophysics Data System (ADS)

Wu, S.; Mclaskey, G.

2017-12-01

We investigate foreshocks and aftershocks of dynamic stick-slip events generated on a newly constructed 3 m biaxial friction apparatus at Cornell University (attached figure). In a typical experiment, two rectangular granite blocks are squeezed together under 4 or 7 MPa of normal pressure ( 4 or 7 million N on a 1 m2 fault surface), and then shear stress is increased until the fault slips 10 - 400 microns in a dynamic rupture event similar to a M -2 to M -3 earthquake. Some ruptures nucleate near the north end of the fault, where the shear force is applied, other ruptures nucleate 2 m from the north end of the fault. The samples are instrumented with 16 piezoelectric sensors, 16 eddy current sensors, and 8 strain gage rosettes, evenly placed along the fault to measure vertical ground motion, local slip, and local stress, respectively. We studied sequences of tens of slip events and identified a total of 194 foreshocks and 66 aftershocks located within 6 s time windows around the stick-slip events and analyzed their timing and locations relative to the quasistatic nucleation process. We found that the locations of the foreshocks and aftershocks were distributed all along the length of the fault, with the majority located at the ends of the fault where local normal and shear stress is highest (caused by both edge effects and the finite stiffness of the steel frame surrounding the granite blocks). We also opened the laboratory fault and inspected the fault surface and found increased wear at the sample ends. To explore the foreshocks' and aftershocks' relationship to the nucleation and afterslip, we compared the occurrence of foreshocks to the local slip rate on the laboratory fault closest to each foreshock in space and time. We found that that majority of foreshocks were generated from local slip rates between 1 and 100 microns/s, though we were not able to resolve slip rate lower than about 1 micron/s. Our experiments provide insight into how foreshocks and aftershocks in natural earthquakes may be influenced both by fault structure and slow slip associated with nucleation or afterslip.

The Maurice field: New gas reserves from buried structure along the Oligocene trend of southwestern Louisiana

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prescott, M.P.

1990-09-01

Significant new gas reserves have recently been discovered in the Marginulina texana sands along the Oligocene trend at the Maurice field. Detailed subsurface maps and seismic data are presented to exhibit the extent and nature of this local buried structure and to demonstrate future opportunities along the Oligocene trend. Since discovery in 1988, the MARG. TEX. RC has extended the Maurice field one-half mile south and has encountered over 170 ft of Marginulina texana pay Estimated reserves are in the order of 160 BCFG with limits of the reservoir still unknown. This reserve addition would increase the estimated ultimate ofmore » the Maurice field by over 70% from 220 BCFG to 380 BCFG. Cross sections across the field depict the new reservoir trap as a buried upthrown fault closure with an anticipated gas column of 700 ft. Interpretation of the origin of this local structure is that of a buried rotated fault block on an overall larger depositional structure. Detailed subsurface maps at the Marginulina texana and the overlying Miogypsinoides level are presented. These maps indicate that one common fault block is productive from two different levels. The deeper Marginulina texana sands are trapped on north dip upthrown to a southern boundary fault, Fault B. The overlying Miogypsinoides sands are trapped on south dip downthrown to a northern boundary fault, Fault A. The northern boundary fault, Fault A, was the Marginulina texana expansion fault and rotated that downthrown section to north dip. Because of the difference in dip between the two levels, the apex of the deeper Marginulina texana fault closure is juxtaposed by one mile south relative to the overlying Miogypsinoides fault closure. Analysis indicates that important structural growth occur-red during Marginulina texana deposition with a local unconformity covering the apex of the upthrown fault closure. State-of-the-art reconnaissance seismic data clearly exhibit this buried rotated fault block.« less

Analysis of a hardware and software fault tolerant processor for critical applications

NASA Technical Reports Server (NTRS)

Dugan, Joanne B.

1993-01-01

Computer systems for critical applications must be designed to tolerate software faults as well as hardware faults. A unified approach to tolerating hardware and software faults is characterized by classifying faults in terms of duration (transient or permanent) rather than source (hardware or software). Errors arising from transient faults can be handled through masking or voting, but errors arising from permanent faults require system reconfiguration to bypass the failed component. Most errors which are caused by software faults can be considered transient, in that they are input-dependent. Software faults are triggered by a particular set of inputs. Quantitative dependability analysis of systems which exhibit a unified approach to fault tolerance can be performed by a hierarchical combination of fault tree and Markov models. A methodology for analyzing hardware and software fault tolerant systems is applied to the analysis of a hypothetical system, loosely based on the Fault Tolerant Parallel Processor. The models consider both transient and permanent faults, hardware and software faults, independent and related software faults, automatic recovery, and reconfiguration.

Fault tolerant system based on IDDQ testing

NASA Astrophysics Data System (ADS)

Guibane, Badi; Hamdi, Belgacem; Mtibaa, Abdellatif; Bensalem, Brahim

2018-06-01

Offline test is essential to ensure good manufacturing quality. However, for permanent or transient faults that occur during the use of the integrated circuit in an application, an online integrated test is needed as well. This procedure should ensure the detection and possibly the correction or the masking of these faults. This requirement of self-correction is sometimes necessary, especially in critical applications that require high security such as automotive, space or biomedical applications. We propose a fault-tolerant design for analogue and mixed-signal design complementary metal oxide (CMOS) circuits based on the quiescent current supply (IDDQ) testing. A defect can cause an increase in current consumption. IDDQ testing technique is based on the measurement of power supply current to distinguish between functional and failed circuits. The technique has been an effective testing method for detecting physical defects such as gate-oxide shorts, floating gates (open) and bridging defects in CMOS integrated circuits. An architecture called BICS (Built In Current Sensor) is used for monitoring the supply current (IDDQ) of the connected integrated circuit. If the measured current is not within the normal range, a defect is signalled and the system switches connection from the defective to a functional integrated circuit. The fault-tolerant technique is composed essentially by a double mirror built-in current sensor, allowing the detection of abnormal current consumption and blocks allowing the connection to redundant circuits, if a defect occurs. Spices simulations are performed to valid the proposed design.

Studies on Automobile Clutch Release Bearing Characteristics with Acoustic Emission

NASA Astrophysics Data System (ADS)

Chen, Guoliang; Chen, Xiaoyang

Automobile clutch release bearings are important automotive driveline components. For the clutch release bearing, early fatigue failure diagnosis is significant, but the early fatigue failure response signal is not obvious, because failure signals are susceptible to noise on the transmission path and to working environment factors such as interference. With an improvement in vehicle design, clutch release bearing fatigue life indicators have increasingly become an important requirement. Contact fatigue is the main failure mode of release rolling bearing components. Acoustic emission techniques in contact fatigue failure detection have unique advantages, which include highly sensitive nondestructive testing methods. In the acoustic emission technique to detect a bearing, signals are collected from multiple sensors. Each signal contains partial fault information, and there is overlap between the signals' fault information. Therefore, the sensor signals receive simultaneous source information integration is complete fragment rolling bearing fault acoustic emission signal, which is the key issue of accurate fault diagnosis. Release bearing comprises the following components: the outer ring, inner ring, rolling ball, cage. When a failure occurs (such as cracking, pitting), the other components will impact damaged point to produce acoustic emission signal. Release bearings mainly emit an acoustic emission waveform with a Rayleigh wave propagation. Elastic waves emitted from the sound source, and it is through the part surface bearing scattering. Dynamic simulation of rolling bearing failure will contribute to a more in-depth understanding of the characteristics of rolling bearing failure, because monitoring and fault diagnosis of rolling bearings provide a theoretical basis and foundation.

Table-top earthquakes; a demonstration of seismology for teachers and students that can be used to augment lessons in earth science, physics, math, social studies, geography

USGS Publications Warehouse

Lahr, J.C.

1998-01-01

The apparatus consists of a heavy object that is dragged steadily with an elastic cord. Although pulled with a constant velocity, the heavy object repeatedly slides and then stops. A small vibration sensor, attached to a computer display, graphically monitors this intermittent motion. 2 This intermittent sliding motion mimics the intermittent fault slippage that characterizes the earthquake fault zones. In tectonically active regions, the Earth's outer brittle shell, which is about 50 km thick, is slowly deformed elastically along active faults. As the deformation increases, stress also increases, until fault slippage releases the stored elastic energy. This process is called elastic rebound. Detailed instructions are given for assembly and construction of this demonstration. Included are suggested sources for the vibration sensor (geophone) and the computer interface. Exclusive of the personal computer, the total cost is between $125 and $150. I gave a talk at the Geological Society of America's Cordilleran Section Centennial meeting on June 2, 1999. The slides show how this table-top demonstration can be used to help meet many of the K-12 teaching goals described in Benchmarks for Science Literacy (American Association for the Advancement of Science, 1993).

Data-driven fault detection, isolation and estimation of aircraft gas turbine engine actuator and sensors

NASA Astrophysics Data System (ADS)

Naderi, E.; Khorasani, K.

2018-02-01

In this work, a data-driven fault detection, isolation, and estimation (FDI&E) methodology is proposed and developed specifically for monitoring the aircraft gas turbine engine actuator and sensors. The proposed FDI&E filters are directly constructed by using only the available system I/O data at each operating point of the engine. The healthy gas turbine engine is stimulated by a sinusoidal input containing a limited number of frequencies. First, the associated system Markov parameters are estimated by using the FFT of the input and output signals to obtain the frequency response of the gas turbine engine. These data are then used for direct design and realization of the fault detection, isolation and estimation filters. Our proposed scheme therefore does not require any a priori knowledge of the system linear model or its number of poles and zeros at each operating point. We have investigated the effects of the size of the frequency response data on the performance of our proposed schemes. We have shown through comprehensive case studies simulations that desirable fault detection, isolation and estimation performance metrics defined in terms of the confusion matrix criterion can be achieved by having access to only the frequency response of the system at only a limited number of frequencies.

Real-time diagnostics for a reusable rocket engine

NASA Technical Reports Server (NTRS)

Guo, T. H.; Merrill, W.; Duyar, A.

1992-01-01

A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2.

Corrugated megathrust revealed offshore from Costa Rica

NASA Astrophysics Data System (ADS)

Edwards, Joel H.; Kluesner, Jared W.; Silver, Eli A.; Brodsky, Emily E.; Brothers, Daniel S.; Bangs, Nathan L.; Kirkpatrick, James D.; Wood, Ruby; Okamoto, Kristina

2018-03-01

Exhumed faults are rough, often exhibiting topographic corrugations oriented in the direction of slip; such features are fundamental to mechanical processes that drive earthquakes and fault evolution. However, our understanding of corrugation genesis remains limited due to a lack of in situ observations at depth, especially at subducting plate boundaries. Here we present three-dimensional seismic reflection data of the Costa Rica subduction zone that image a shallow megathrust fault characterized by corrugated, and chaotic and weakly corrugated topographies. The corrugated surfaces extend from near the trench to several kilometres down-dip, exhibit high reflection amplitudes (consistent with high fluid content/pressure) and trend 11-18° oblique to subduction, suggesting 15 to 25 mm yr-1 of trench-parallel slip partitioning across the plate boundary. The corrugations form along portions of the megathrust with greater cumulative slip and may act as fluid conduits. In contrast, weakly corrugated areas occur adjacent to active plate bending faults where the megathrust has migrated up-section, forming a nascent fault surface. The variations in megathrust roughness imaged here suggest that abandonment and then reestablishment of the megathrust up-section transiently increases fault roughness. Analogous corrugations may exist along significant portions of subduction megathrusts globally.

Corrugated megathrust revealed offshore from Costa Rica

USGS Publications Warehouse

Edwards, Joel H.; Kluesner, Jared; Silver, Eli A.; Brodsky, Emily E.; Brothers, Daniel; Bangs, Nathan L.; Kirkpatrick, James D.; Wood, Ruby; Okamato, Kristina

2018-01-01

Exhumed faults are rough, often exhibiting topographic corrugations oriented in the direction of slip; such features are fundamental to mechanical processes that drive earthquakes and fault evolution. However, our understanding of corrugation genesis remains limited due to a lack of in situ observations at depth, especially at subducting plate boundaries. Here we present three-dimensional seismic reflection data of the Costa Rica subduction zone that image a shallow megathrust fault characterized by corrugated, and chaotic and weakly corrugated topographies. The corrugated surfaces extend from near the trench to several kilometres down-dip, exhibit high reflection amplitudes (consistent with high fluid content/pressure) and trend 11–18° oblique to subduction, suggesting 15 to 25 mm yr−1 of trench-parallel slip partitioning across the plate boundary. The corrugations form along portions of the megathrust with greater cumulative slip and may act as fluid conduits. In contrast, weakly corrugated areas occur adjacent to active plate bending faults where the megathrust has migrated up-section, forming a nascent fault surface. The variations in megathrust roughness imaged here suggest that abandonment and then reestablishment of the megathrust up-section transiently increases fault roughness. Analogous corrugations may exist along significant portions of subduction megathrusts globally.

High Speed Operation and Testing of a Fault Tolerant Magnetic Bearing

NASA Technical Reports Server (NTRS)

DeWitt, Kenneth; Clark, Daniel

2004-01-01

Research activities undertaken to upgrade the fault-tolerant facility, continue testing high-speed fault-tolerant operation, and assist in the commission of the high temperature (1000 degrees F) thrust magnetic bearing as described. The fault-tolerant magnetic bearing test facility was upgraded to operate to 40,000 RPM. The necessary upgrades included new state-of-the art position sensors with high frequency modulation and new power edge filtering of amplifier outputs. A comparison study of the new sensors and the previous system was done as well as a noise assessment of the sensor-to-controller signals. Also a comparison study of power edge filtering for amplifier-to-actuator signals was done; this information is valuable for all position sensing and motor actuation applications. After these facility upgrades were completed, the rig is believed to have capabilities for 40,000 RPM operation, though this has yet to be demonstrated. Other upgrades included verification and upgrading of safety shielding, and upgrading control algorithms. The rig will now also be used to demonstrate motoring capabilities and control algorithms are in the process of being created. Recently an extreme temperature thrust magnetic bearing was designed from the ground up. The thrust bearing was designed to fit within the existing high temperature facility. The retrofit began near the end of the summer, 04, and continues currently. Contract staff authored a NASA-TM entitled "An Overview of Magnetic Bearing Technology for Gas Turbine Engines", containing a compilation of bearing data as it pertains to operation in the regime of the gas turbine engine and a presentation of how magnetic bearings can become a viable candidate for use in future engine technology.

Map and data for Quaternary faults and folds in Washington state

USGS Publications Warehouse

Lidke, David J.; Johnson, Samuel Y.; McCrory, Patricia A.; Personius, Stephen F.; Nelson, Alan R.; Dart, Richard L.; Bradley, Lee-Ann; Haller, Kathleen M.; Machette, Michael N.

2004-01-01

The map shows faults and folds in Washington State that exhibit evidence of Quaternary deformation and includes data on timing of most recent movement, sense of movement, slip rate, and continuity of surface expression.

Optimization of Second Fault Detection Thresholds to Maximize Mission POS

NASA Technical Reports Server (NTRS)

Anzalone, Evan

2018-01-01

In order to support manned spaceflight safety requirements, the Space Launch System (SLS) has defined program-level requirements for key systems to ensure successful operation under single fault conditions. To accommodate this with regards to Navigation, the SLS utilizes an internally redundant Inertial Navigation System (INS) with built-in capability to detect, isolate, and recover from first failure conditions and still maintain adherence to performance requirements. The unit utilizes multiple hardware- and software-level techniques to enable detection, isolation, and recovery from these events in terms of its built-in Fault Detection, Isolation, and Recovery (FDIR) algorithms. Successful operation is defined in terms of sufficient navigation accuracy at insertion while operating under worst case single sensor outages (gyroscope and accelerometer faults at launch). In addition to first fault detection and recovery, the SLS program has also levied requirements relating to the capability of the INS to detect a second fault, tracking any unacceptable uncertainty in knowledge of the vehicle's state. This detection functionality is required in order to feed abort analysis and ensure crew safety. Increases in navigation state error and sensor faults can drive the vehicle outside of its operational as-designed environments and outside of its performance envelope causing loss of mission, or worse, loss of crew. The criteria for operation under second faults allows for a larger set of achievable missions in terms of potential fault conditions, due to the INS operating at the edge of its capability. As this performance is defined and controlled at the vehicle level, it allows for the use of system level margins to increase probability of mission success on the operational edges of the design space. Due to the implications of the vehicle response to abort conditions (such as a potentially failed INS), it is important to consider a wide range of failure scenarios in terms of both magnitude and time. As such, the Navigation team is taking advantage of the INS's capability to schedule and change fault detection thresholds in flight. These values are optimized along a nominal trajectory in order to maximize probability of mission success, and reducing the probability of false positives (defined as when the INS would report a second fault condition resulting in loss of mission, but the vehicle would still meet insertion requirements within system-level margins). This paper will describe an optimization approach using Genetic Algorithms to tune the threshold parameters to maximize vehicle resilience to second fault events as a function of potential fault magnitude and time of fault over an ascent mission profile. The analysis approach, and performance assessment of the results will be presented to demonstrate the applicability of this process to second fault detection to maximize mission probability of success.

Frictional properties of low-angle normal fault gouges and implications for low-angle normal fault slip

NASA Astrophysics Data System (ADS)

Haines, Samuel; Marone, Chris; Saffer, Demian

2014-12-01

The mechanics of slip on low-angle normal faults (LANFs) remain an enduring problem in structural geology and fault mechanics. In most cases, new faults should form rather than having slip occur on LANFs, assuming values of fault friction consistent with Byerlee's Law. We present results of laboratory measurements on the frictional properties of natural clay-rich gouges from low-angle normal faults (LANF) in the American Cordillera, from the Whipple Mts. Detachment, the Panamint range-front detachment, and the Waterman Hills detachment. These clay-rich gouges are dominated by neoformed clay minerals and are an integral part of fault zones in many LANFs, yet their frictional properties under in situ conditions remain relatively unknown. We conducted measurements under saturated and controlled pore pressure conditions at effective normal stresses ranging from 20 to 60 MPa (corresponding to depths of 0.9-2.9 km), on both powdered and intact wafers of fault rock. For the Whipple Mountains detachment, friction coefficient (μ) varies depending on clast content, with values ranging from 0.40 to 0.58 for clast-rich material, and 0.29-0.30 for clay-rich gouge. Samples from the Panamint range-front detachment were clay-rich, and exhibit friction values of 0.28 to 0.38, significantly lower than reported from previous studies on fault gouges tested under room humidity (nominally dry) conditions, including samples from the same exposure. Samples from the Waterman Hills detachment are slightly stronger, with μ ranging from 0.38 to 0.43. The neoformed gouge materials from all three localities exhibits velocity-strengthening frictional behavior under almost all of the experimental conditions we explored, with values of the friction rate parameter (a - b) ranging from -0.001 to +0.025. Clast-rich samples exhibited frictional healing (strength increases with hold time), whereas clay-rich samples do not. Our results indicate that where clay-rich neoformed gouges are present along LANFs, they provide a mechanically viable explanation for slip on faults with dips <20°, requiring only moderate (Pf <σ3) overpressures and/or correcting for ∼5° of footwall tilting. Furthermore, the low rates of frictional strength recovery and velocity-strengthening frictional behavior we observe provide an explanation for the lack of observed seismicity on these structures. We suggest that LANFs in the upper crust (depth <8 km) slip via a combination of a) reaction-weakening of initially high-angle fault zones by the formation of neoformed clay-rich gouges, and b) regional tectonic accommodation of rotating fault blocks.

Trust index based fault tolerant multiple event localization algorithm for WSNs.

PubMed

Xu, Xianghua; Gao, Xueyong; Wan, Jian; Xiong, Naixue

2011-01-01

This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms.

Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs

PubMed Central

Xu, Xianghua; Gao, Xueyong; Wan, Jian; Xiong, Naixue

2011-01-01

This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms. PMID:22163972

Steady-state analysis of a faulted three-phase four-wire system supplying induction motors with neutrals connected and other single-phase line-to-neutral loads

NASA Technical Reports Server (NTRS)

Wood, M. E.

1980-01-01

Four wire Wye connected ac power systems exhibit peculiar steady state fault characteristics when the fourth wire of three phase induction motors is connected. The loss of one phase of power source due to a series or shunt fault results in currents higher than anticipated on the remaining two phases. A theoretical approach to compute the fault currents and voltages is developed. A FORTRAN program is included in the appendix.

Reset Tree-Based Optical Fault Detection

PubMed Central

Lee, Dong-Geon; Choi, Dooho; Seo, Jungtaek; Kim, Howon

2013-01-01

In this paper, we present a new reset tree-based scheme to protect cryptographic hardware against optical fault injection attacks. As one of the most powerful invasive attacks on cryptographic hardware, optical fault attacks cause semiconductors to misbehave by injecting high-energy light into a decapped integrated circuit. The contaminated result from the affected chip is then used to reveal secret information, such as a key, from the cryptographic hardware. Since the advent of such attacks, various countermeasures have been proposed. Although most of these countermeasures are strong, there is still the possibility of attack. In this paper, we present a novel optical fault detection scheme that utilizes the buffers on a circuit's reset signal tree as a fault detection sensor. To evaluate our proposal, we model radiation-induced currents into circuit components and perform a SPICE simulation. The proposed scheme is expected to be used as a supplemental security tool. PMID:23698267

Partitioning in Avionics Architectures: Requirements, Mechanisms, and Assurance

NASA Technical Reports Server (NTRS)

Rushby, John

1999-01-01

Automated aircraft control has traditionally been divided into distinct "functions" that are implemented separately (e.g., autopilot, autothrottle, flight management); each function has its own fault-tolerant computer system, and dependencies among different functions are generally limited to the exchange of sensor and control data. A by-product of this "federated" architecture is that faults are strongly contained within the computer system of the function where they occur and cannot readily propagate to affect the operation of other functions. More modern avionics architectures contemplate supporting multiple functions on a single, shared, fault-tolerant computer system where natural fault containment boundaries are less sharply defined. Partitioning uses appropriate hardware and software mechanisms to restore strong fault containment to such integrated architectures. This report examines the requirements for partitioning, mechanisms for their realization, and issues in providing assurance for partitioning. Because partitioning shares some concerns with computer security, security models are reviewed and compared with the concerns of partitioning.

Velocity Gradient Across the San Andreas Fault and Changes in Slip Behavior as Outlined by Full non Linear Tomography

NASA Astrophysics Data System (ADS)

Chiarabba, C.; Giacomuzzi, G.; Piana Agostinetti, N.

2017-12-01

The San Andreas Fault (SAF) near Parkfield is the best known fault section which exhibit a clear transition in slip behavior from stable to unstable. Intensive monitoring and decades of studies permit to identify details of these processes with a good definition of fault structure and subsurface models. Tomographic models computed so far revealed the existence of large velocity contrasts, yielding physical insight on fault rheology. In this study, we applied a recently developed full non-linear tomography method to compute Vp and Vs models which focus on the section of the fault that exhibit fault slip transition. The new tomographic code allows not to impose a vertical seismic discontinuity at the fault position, as routinely done in linearized codes. Any lateral velocity contrast found is directly dictated by the data themselves and not imposed by subjective choices. The use of the same dataset of previous tomographic studies allows a proper comparison of results. We use a total of 861 earthquakes, 72 blasts and 82 shots and the overall arrival time dataset consists of 43948 P- and 29158 S-wave arrival times, accurately selected to take care of seismic anisotropy. Computed Vp and Vp/Vs models, which by-pass the main problems related to linarized LET algorithms, excellently match independent available constraints and show crustal heterogeneities with a high resolution. The high resolution obtained in the fault surroundings permits to infer lateral changes of Vp and Vp/Vs across the fault (velocity gradient). We observe that stable and unstable sliding sections of the SAF have different velocity gradients, small and negligible in the stable slip segment, but larger than 15 % in the unstable slip segment. Our results suggest that Vp and Vp/Vs gradients across the fault control fault rheology and the attitude of fault slip behavior.

A Fault Recognition System for Gearboxes of Wind Turbines

NASA Astrophysics Data System (ADS)

Yang, Zhiling; Huang, Haiyue; Yin, Zidong

2017-12-01

Costs of maintenance and loss of power generation caused by the faults of wind turbines gearboxes are the main components of operation costs for a wind farm. Therefore, the technology of condition monitoring and fault recognition for wind turbines gearboxes is becoming a hot topic. A condition monitoring and fault recognition system (CMFRS) is presented for CBM of wind turbines gearboxes in this paper. The vibration signals from acceleration sensors at different locations of gearbox and the data from supervisory control and data acquisition (SCADA) system are collected to CMFRS. Then the feature extraction and optimization algorithm is applied to these operational data. Furthermore, to recognize the fault of gearboxes, the GSO-LSSVR algorithm is proposed, combining the least squares support vector regression machine (LSSVR) with the Glowworm Swarm Optimization (GSO) algorithm. Finally, the results show that the fault recognition system used in this paper has a high rate for identifying three states of wind turbines’ gears; besides, the combination of date features can affect the identifying rate and the selection optimization algorithm presented in this paper can get a pretty good date feature subset for the fault recognition.

Remote sensing in hydrology: A survey of applications with selected bibliography and abstracts

NASA Technical Reports Server (NTRS)

Sers, S. W. (Compiler)

1971-01-01

Remote infrared sensing as a water exploration technique is demonstrated. Various applications are described, demonstrating that infrared sensors can locate aquifers, geothermal water, water trapped by faults, springs and water in desert regions. The potentiality of airborne IR sensors as a water prospecting tool is considered. Also included is a selected bibliography with abstracts concentrating on those publications which will better acquaint the hydrologist with investigations using thermal remote sensors as applied to water exploration.

Oil pipeline geohazard monitoring using optical fiber FBG strain sensors (Conference Presentation)

NASA Astrophysics Data System (ADS)

Salazar-Ferro, Andres; Mendez, Alexis

2016-04-01

Pipelines are naturally vulnerable to operational, environmental and man-made effects such as internal erosion and corrosion; mechanical deformation due to geophysical risks and ground movements; leaks from neglect and vandalism; as well as encroachments from nearby excavations or illegal intrusions. The actual detection and localization of incipient and advanced faults in pipelines is a very difficult, expensive and inexact task. Anything that operators can do to mitigate the effects of these faults will provide increased reliability, reduced downtime and maintenance costs, as well as increased revenues. This talk will review the on-line monitoring of an extensive network of oil pipelines in service in Colombia using optical fiber Bragg grating (FBG) strain sensors for the measurement of strains and bending caused by geohazard risks such as soil movements, landslides, settlements, flooding and seismic activity. The FBG sensors were mounted on the outside of the pipelines at discrete locations where geohazard risk was expected. The system has been in service for the past 3 years with over 1,000 strain sensors mounted. The technique has been reliable and effective in giving advanced warning of accumulated pipeline strains as well as possible ruptures.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

PubMed

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-06-17

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

PubMed Central

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-01-01

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.

PubMed

Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

2015-01-01

The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

PubMed Central

Yuan, Xianfeng; Song, Mumin; Chen, Zhumin; Li, Yan

2015-01-01

The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

The reflection of evolving bearing faults in the stator current's extended park vector approach for induction machines

NASA Astrophysics Data System (ADS)

Corne, Bram; Vervisch, Bram; Derammelaere, Stijn; Knockaert, Jos; Desmet, Jan

2018-07-01

Stator current analysis has the potential of becoming the most cost-effective condition monitoring technology regarding electric rotating machinery. Since both electrical and mechanical faults are detected by inexpensive and robust current-sensors, measuring current is advantageous on other techniques such as vibration, acoustic or temperature analysis. However, this technology is struggling to breach into the market of condition monitoring as the electrical interpretation of mechanical machine-problems is highly complicated. Recently, the authors built a test-rig which facilitates the emulation of several representative mechanical faults on an 11 kW induction machine with high accuracy and reproducibility. Operating this test-rig, the stator current of the induction machine under test can be analyzed while mechanical faults are emulated. Furthermore, while emulating, the fault-severity can be manipulated adaptively under controllable environmental conditions. This creates the opportunity of examining the relation between the magnitude of the well-known current fault components and the corresponding fault-severity. This paper presents the emulation of evolving bearing faults and their reflection in the Extended Park Vector Approach for the 11 kW induction machine under test. The results confirm the strong relation between the bearing faults and the stator current fault components in both identification and fault-severity. Conclusively, stator current analysis increases reliability in the application as a complete, robust, on-line condition monitoring technology.

Arc burst pattern analysis fault detection system

NASA Technical Reports Server (NTRS)

Russell, B. Don (Inventor); Aucoin, B. Michael (Inventor); Benner, Carl L. (Inventor)

1997-01-01

A method and apparatus are provided for detecting an arcing fault on a power line carrying a load current. Parameters indicative of power flow and possible fault events on the line, such as voltage and load current, are monitored and analyzed for an arc burst pattern exhibited by arcing faults in a power system. These arcing faults are detected by identifying bursts of each half-cycle of the fundamental current. Bursts occurring at or near a voltage peak indicate arcing on that phase. Once a faulted phase line is identified, a comparison of the current and voltage reveals whether the fault is located in a downstream direction of power flow toward customers, or upstream toward a generation station. If the fault is located downstream, the line is de-energized, and if located upstream, the line may remain energized to prevent unnecessary power outages.

Detection of Rooftop Cooling Unit Faults Based on Electrical Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armstrong, Peter R.; Laughman, C R.; Leeb, S B.

Non-intrusive load monitoring (NILM) is accomplished by sampling voltage and current at high rates and reducing the resulting start transients or harmonic contents to concise ''signatures''. Changes in these signatures can be used to detect, and in many cases directly diagnose, equipment and component faults associated with roof-top cooling units. Use of the NILM for fault detection and diagnosis (FDD) is important because (1) it complements other FDD schemes that are based on thermo-fluid sensors and analyses and (2) it is minimally intrusive (one measuring point in the relatively protected confines of the control panel) and therefore inherently reliable. Thismore » paper describes changes in the power signatures of fans and compressors that were found, experimentally and theoretically, to be useful for fault detection.« less

Health management and controls for earth to orbit propulsion systems

NASA Technical Reports Server (NTRS)

Bickford, R. L.

1992-01-01

Fault detection and isolation for advanced rocket engine controllers are discussed focusing on advanced sensing systems and software which significantly improve component failure detection for engine safety and health management. Aerojet's Space Transportation Main Engine controller for the National Launch System is the state of the art in fault tolerant engine avionics. Health management systems provide high levels of automated fault coverage and significantly improve vehicle delivered reliability and lower preflight operations costs. Key technologies, including the sensor data validation algorithms and flight capable spectrometers, have been demonstrated in ground applications and are found to be suitable for bridging programs into flight applications.

Intelligent fault isolation and diagnosis for communication satellite systems

NASA Technical Reports Server (NTRS)

Tallo, Donald P.; Durkin, John; Petrik, Edward J.

1992-01-01

Discussed here is a prototype diagnosis expert system to provide the Advanced Communication Technology Satellite (ACTS) System with autonomous diagnosis capability. The system, the Fault Isolation and Diagnosis EXpert (FIDEX) system, is a frame-based system that uses hierarchical structures to represent such items as the satellite's subsystems, components, sensors, and fault states. This overall frame architecture integrates the hierarchical structures into a lattice that provides a flexible representation scheme and facilitates system maintenance. FIDEX uses an inexact reasoning technique based on the incrementally acquired evidence approach developed by Shortliffe. The system is designed with a primitive learning ability through which it maintains a record of past diagnosis studies.

Source Rupture Models and Tsunami Simulations of Destructive October 28, 2012 Queen Charlotte Islands, British Columbia (Mw: 7.8) and September 16, 2015 Illapel, Chile (Mw: 8.3) Earthquakes

NASA Astrophysics Data System (ADS)

Taymaz, Tuncay; Yolsal-Çevikbilen, Seda; Ulutaş, Ergin

2016-04-01

The finite-fault source rupture models and numerical simulations of tsunami waves generated by 28 October 2012 Queen Charlotte Islands (Mw: 7.8), and 16 September 2015 Illapel-Chile (Mw: 8.3) earthquakes are presented. These subduction zone earthquakes have reverse faulting mechanisms with small amount of strike-slip components which clearly reflect the characteristics of convergence zones. The finite-fault slip models of the 2012 Queen Charlotte and 2015 Chile earthquakes are estimated from a back-projection method that uses teleseismic P- waveforms to integrate the direct P-phase with reflected phases from structural discontinuities near the source. Non-uniform rupture models of the fault plane, which are obtained from the finite fault modeling, are used in order to describe the vertical displacement on seabed. In general, the vertical displacement of water surface was considered to be the same as ocean bottom displacement, and it is assumed to be responsible for the initial water surface deformation gives rise to occurrence of tsunami waves. In this study, it was calculated by using the elastic dislocation algorithm. The results of numerical tsunami simulations are compared with tide gauges and Deep-ocean Assessment and Reporting of Tsunami (DART) buoy records. De-tiding, de-trending, low-pass and high-pass filters were applied to detect tsunami waves in deep ocean sensors and tide gauge records. As an example, the observed records and results of simulations showed that the 2012 Queen Charlotte Islands earthquake generated about 1 meter tsunami-waves in Maui and Hilo (Hawaii), 5 hours and 30 minutes after the earthquake. Furthermore, the calculated amplitudes and time series of the tsunami waves of the recent 2015 Illapel (Chile) earthquake are exhibiting good agreement with the records of tide and DART gauges except at stations Valparaiso and Pichidangui (Chile). This project is supported by The Scientific and Technological Research Council of Turkey (TUBITAK Project No: CAYDAG-114Y066).

High-velocity frictional properties of Alpine Fault rocks: Mechanical data, microstructural analysis, and implications for rupture propagation

NASA Astrophysics Data System (ADS)

Boulton, Carolyn; Yao, Lu; Faulkner, Daniel R.; Townend, John; Toy, Virginia G.; Sutherland, Rupert; Ma, Shengli; Shimamoto, Toshihiko

2017-04-01

The Alpine Fault in New Zealand is a major plate-bounding structure that typically slips in ∼M8 earthquakes every c. 330 years. To investigate the near-surface, high-velocity frictional behavior of surface- and borehole-derived Alpine Fault gouges and cataclasites, twenty-one rotary shear experiments were conducted at 1 MPa normal stress and 1 m/s equivalent slip velocity under both room-dry and water-saturated (wet) conditions. In the room-dry experiments, the peak friction coefficient (μp = τp/σn) of Alpine Fault cataclasites and fault gouges was consistently high (mean μp = 0.67 ± 0.07). In the wet experiments, the fault gouge peak friction coefficients were lower (mean μp = 0.20 ± 0.12) than the cataclasite peak friction coefficients (mean μp = 0.64 ± 0.04). All fault rocks exhibited very low steady-state friction coefficients (μss) (room-dry experiments mean μss = 0.16 ± 0.05; wet experiments mean μss = 0.09 ± 0.04). Of all the experiments performed, six experiments conducted on wet smectite-bearing principal slip zone (PSZ) fault gouges yielded the lowest peak friction coefficients (μp = 0.10-0.20), the lowest steady-state friction coefficients (μss = 0.03-0.09), and, commonly, the lowest specific fracture energy values (EG = 0.01-0.69 MJ/m2). Microstructures produced during room-dry and wet experiments on a smectite-bearing PSZ fault gouge were compared with microstructures in the same material recovered from the Deep Fault Drilling Project (DFDP-1) drill cores. The near-absence of localized shear bands with a strong crystallographic preferred orientation in the natural samples most resembles microstructures formed during wet experiments. Mechanical data and microstructural observations suggest that Alpine Fault ruptures propagate preferentially through water-saturated smectite-bearing fault gouges that exhibit low peak and steady-state friction coefficients.

The 7.9 Denali Fault, Alaska Earthquake of November 3, 2002: Aftershock Locations, Moment Tensors and Focal Mechanisms from the Regional Seismic Network Data

NASA Astrophysics Data System (ADS)

Ratchkovski, N. A.; Hansen, R. A.; Kore, K. R.

2003-04-01

The largest earthquake ever recorded on the Denali fault system (magnitude 7.9) struck central Alaska on November 3, 2002. It was preceded by a magnitude 6.7 earthquake on October 23. This earlier earthquake and its zone of aftershocks were located ~20 km to the west of the 7.9 quake. Aftershock locations and surface slip observations from the 7.9 quake indicate that the rupture was predominately unilateral in the eastward direction. The geologists mapped a ~300-km-long rupture and measured maximum offsets of 8.8 meters. The 7.9 event ruptured three different faults. The rupture began on the northeast trending Susitna Glacier Thrust fault, a splay fault south of the Denali fault. Then the rupture transferred to the Denali fault and propagated eastward for 220 km. At about 143W the rupture moved onto the adjacent southeast-trending Totschunda fault and propagated for another 55 km. The cumulative length of the 6.7 and 7.9 aftershock zones along the Denali and Totschunda faults is about 380 km. The earthquakes were recorded and processed by the Alaska Earthquake Information Center (AEIC). The AEIC acquires and processes data from the Alaska Seismic Network, consisting of over 350 seismograph stations. Nearly 40 of these sites are equipped with the broad-band sensors, some of which also have strong motion sensors. The rest of the stations are either 1 or 3-component short-period instruments. The data from these stations are collected, processed and archived at the AEIC. The AEIC staff installed a temporary seismic network of 6 instruments following the 6.7 earthquake and an additional 20 stations following the 7.9 earthquake. Prior to the 7.9 Denali Fault event, the AEIC was locating 35 to 50 events per day. After the event, the processing load increased to over 300 events per day during the first week following the event. In this presentation, we will present and interpret the aftershock location patterns, first motion focal mechanism solutions, and regional seismic moment tensors for the larger events. We used the double difference method to relocate aftershocks of both the 6.7 and 7.9 events. The relocated aftershocks indicate complex faulting along the rupture zone. The aftershocks are located not only along the main rupture zone, but also illuminate multiple splay faults north and south of the Denali fault. We calculated principal stress directions along the Denali fault both before and after the 7.9 event from the focal mechanisms. The stress orientations before and after the event are nearly identical. The maximum horizontal compressive stress is nearly normal to the trace of the Denali fault and rotates gradually from NW orientation at the western end of the rupture zone to NE orientation near the junction with the Totschunda fault.

A new debris sensor based on dual excitation sources for online debris monitoring

NASA Astrophysics Data System (ADS)

Hong, Wei; Wang, Shaoping; Tomovic, Mileta M.; Liu, Haokuo; Wang, Xingjian

2015-09-01

Mechanical systems could be severely damaged by loose debris generated through wear processes between contact surfaces. Hence, debris detection is necessary for effective fault diagnosis, life prediction, and prevention of catastrophic failures. This paper presents a new in-line debris sensor for hydraulic systems based on dual excitation sources. The proposed sensor makes magnetic lines more concentrated while at the same time improving magnetic field uniformity. As a result the sensor has higher sensitivity and improved precision. This paper develops the sensor model, discusses sensor structural features, and introduces a measurement method for debris size identification. Finally, experimental verification is presented indicating that that the sensor can effectively detect 81 μm (cube) or larger particles in 12 mm outside diameter (OD) organic glass pipe.

The application of micromachined sensors to manned space systems

NASA Technical Reports Server (NTRS)

Bordano, Aldo; Havey, Gary; Wald, Jerry; Nasr, Hatem

1993-01-01

Micromachined sensors promise significant system advantages to manned space vehicles. Vehicle Health Monitoring (VHM) is a critical need for most future space systems. Micromachined sensors play a significant role in advancing the application of VHM in future space vehicles. This paper addresses the requirements that future VHM systems place on micromachined sensors such as: system integration, performance, size, weight, power, redundancy, reliability and fault tolerance. Current uses of micromachined sensors in commercial, military and space systems are used to document advantages that are gained and lessons learned. Based on these successes, the future use of micromachined sensors in space programs is discussed in terms of future directions and issues that need to be addressed such as how commercial and military sensors can meet future space system requirements.

Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

PubMed Central

Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao

2014-01-01

Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately. PMID:24936949

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier.

PubMed

Huang, Nantian; Chen, Huaijin; Cai, Guowei; Fang, Lihua; Wang, Yuqiang

2016-11-10

Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD) and multi-layer classifier (MLC) is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs). The IMF matrix is divided into submatrices to compute the local singular values (LSV). The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs) and a support vector machine (SVM) is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF₆ HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever) are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods.

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier

PubMed Central

Huang, Nantian; Chen, Huaijin; Cai, Guowei; Fang, Lihua; Wang, Yuqiang

2016-01-01

Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD) and multi-layer classifier (MLC) is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs). The IMF matrix is divided into submatrices to compute the local singular values (LSV). The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs) and a support vector machine (SVM) is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF6 HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever) are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods. PMID:27834902

Moving Closer to EarthScope: A Major New Initiative for the Earth Sciences*

NASA Astrophysics Data System (ADS)

Simpson, D.; Blewitt, G.; Ekstrom, G.; Henyey, T.; Hickman, S.; Prescott, W.; Zoback, M.

2002-12-01

EarthScope is a scientific research and infrastructure initiative designed to provide a suite of new observational facilities to address fundamental questions about the evolution of continents and the processes responsible for earthquakes and volcanic eruptions. The integrated observing systems that will comprise EarthScope capitalize on recent developments in sensor technology and communications to provide Earth scientists with synoptic and high-resolution data derived from a variety of geophysical sensors. An array of 400 broadband seismometers will spend more than ten years crossing the contiguous 48 states and Alaska to image features that make up the internal structure of the continent and underlying mantle. Additional seismic and electromagnetic instrumentation will be available for high resolution imaging of geological targets of special interest. A network of continuously recording Global Positioning System (GPS) receivers and sensitive borehole strainmeters will be installed along the western U.S. plate boundary. These sensors will measure how western North America is deforming, what motions occur along faults, how earthquakes start, and how magma flows beneath active volcanoes. A four-kilometer deep observatory bored directly into the San Andreas fault will provide the first opportunity to observe directly the conditions under which earthquakes occur, to collect fault rocks and fluids for laboratory study, and to monitor continuously an active fault zone at depth. All data from the EarthScope facilities will be openly available in real-time to maximize participation from the scientific community and to provide on-going educational outreach to students and the public. EarthScope's sensors will revolutionize observational Earth science in terms of the quantity, quality and spatial extent of the data they provide. Turning these data into exciting scientific discovery will require new modes of experimentation and interdisciplinary cooperation from the Earth science community. A broad sector of the university research community has joined with federal agencies to stimulate the development of facility plans and move EarthScope forward as a coordinated national initiative. With strong prospects for funding next year, the time is right for bold new ideas on how to maximize the use of these powerful new resources in the Earth scientist's toolkit. * On behalf of the EarthScope Working Group

System Wide Joint Position Sensor Fault Tolerance in Robot Systems Using Cartesian Accelerometers

NASA Technical Reports Server (NTRS)

Aldridge, Hal A.; Juang, Jer-Nan

1997-01-01

Joint position sensors are necessary for most robot control systems. A single position sensor failure in a normal robot system can greatly degrade performance. This paper presents a method to obtain position information from Cartesian accelerometers without integration. Depending on the number and location of the accelerometers. the proposed system can tolerate the loss of multiple position sensors. A solution technique suitable for real-time implementation is presented. Simulations were conducted using 5 triaxial accelerometers to recover from the loss of up to 4 joint position sensors on a 7 degree of freedom robot moving in general three dimensional space. The simulations show good estimation performance using non-ideal accelerometer measurements.

Fallback options for airgap sensor fault of an electromagnetic suspension system

NASA Astrophysics Data System (ADS)

Michail, Konstantinos; Zolotas, Argyrios C.; Goodall, Roger M.

2013-06-01

The paper presents a method to recover the performance of an electromagnetic suspension under faulty airgap sensor. The proposed control scheme is a combination of classical control loops, a Kalman Estimator and analytical redundancy (for the airgap signal). In this way redundant airgap sensors are not essential for reliable operation of this system. When the airgap sensor fails the required signal is recovered using a combination of a Kalman estimator and analytical redundancy. The performance of the suspension is optimised using genetic algorithms and some preliminary robustness issues to load and operating airgap variations are discussed. Simulations on a realistic model of such type of suspension illustrate the efficacy of the proposed sensor tolerant control method.

The repetition of large-earthquake ruptures.

PubMed Central

Sieh, K

1996-01-01

This survey of well-documented repeated fault rupture confirms that some faults have exhibited a "characteristic" behavior during repeated large earthquakes--that is, the magnitude, distribution, and style of slip on the fault has repeated during two or more consecutive events. In two cases faults exhibit slip functions that vary little from earthquake to earthquake. In one other well-documented case, however, fault lengths contrast markedly for two consecutive ruptures, but the amount of offset at individual sites was similar. Adjacent individual patches, 10 km or more in length, failed singly during one event and in tandem during the other. More complex cases of repetition may also represent the failure of several distinct patches. The faults of the 1992 Landers earthquake provide an instructive example of such complexity. Together, these examples suggest that large earthquakes commonly result from the failure of one or more patches, each characterized by a slip function that is roughly invariant through consecutive earthquake cycles. The persistence of these slip-patches through two or more large earthquakes indicates that some quasi-invariant physical property controls the pattern and magnitude of slip. These data seem incompatible with theoretical models that produce slip distributions that are highly variable in consecutive large events. Images Fig. 3 Fig. 7 Fig. 9 PMID:11607662

Laboratory studies of frictional sliding and the implications of precursory seismicity

NASA Astrophysics Data System (ADS)

Selvadurai, Paul A.

The dynamic transition from slow to rapid sliding along a frictional interface is of interest to geophysicists, engineers and scientists alike. In our direct shear experiment, we simulated a pre-existing frictional fault similar to those occurring naturally in the Earth. The laboratory study reported here has incorporated appropriate sensors that can detect foreshock events on the fringe of a nucleation zone prior to a gross fault rupture (main shock). During loading we observed foreshocks sequences as slip transitioned from slow to rapid sliding. These laboratory-induced foreshocks showed similar acoustic characteristics and spatio-temporal evolution as those detected in nature. Through direct observation (video camera), foreshocks were found to be the rapid, localized (millimeter length scale) failure of highly stresses asperities formed along the interface. The interface was created by the meshing of two rough polymethyl methacrylate (PMMA) bodies in a direct shear configuration. A carefully calibrated pressure sensitive film was used to map the contact junctions (asperities) throughout the interface at a range of applied normal loads Fn. Foreshocks were found to coalesce in a region of the fault that exhibited a more dense distribution of asperities (referred to as the seismogenic region). Microscopy of the interface in the seismogenic region displayed a variety of surface roughness at various length scales. This may have been introduced from the surface preparation techniques use to create a mature interface. The mature interface consisted of 'flat-topped' asperity regions with separating sharp valleys. The 'flat-topped' sections spanned millimetric length scales and were considerably flatter (nanometric roughness) that the roughness exhibited at longer length scales (tens of millimeters). We believe that the smoother, 'flat-topped' sections were responsible for the individual asperity formation (determining their size and strength), whereas the longer length scale roughness influenced the asperity-asperity interaction during the nucleation phase. Asperities in the seismogenic region where shown to exist close enough to each other so that elastic communication (through the off-fault material) could not be neglected. Prior to gross fault rupture (i.e. mainshock), we measured the propagation of a slow nucleating rupture into the relatively 'locked', seimsogenic region of the fault. Slow slip dynamics were captured using slip sensors placed along the fault that measured a non-uniform slip profile leading up to failure. We found that the propagation of the slow rupture into the locked region was dependent on the normal force Fn. Higher Fn was found to slow the propagation of shear rupture into the locked region. Within the relatively 'locked' region, a noticeable increase in size and a more compact spatial-temporal distribution of foreshocks were measured when Fn was increased. In order to develop an understanding of the relationship between Fn and the resistance of the fault to slow rupture, a quasi-static finite element (FE) model was developed. The model used distributions of asperities measured directly from the pressure sensitive film in a small section of the interface where foreshocks coalesced; specifically, the region where the slowly propagating slip front encountered the more dense distribution of asperities. A single asperity was modeled and followed the Cattaneo partial slip asperity solution. As the shear force increased along the fault, the asperities in this model were able to accommodate tangential slip by entering a partial sliding regime; the central contact of the asperities remained adhered while sliding accumulated along its periphery. Partial slip on the asperity propagated inwards as the shear force was incrementally increased. A further increase in the shear force caused the asperity to enter a full sliding condition. Increasing confining loads caused increased stiffness and increased capacity to store potential shear strain energy -- a possible measure of the 'degree of coupling' between the fault surfaces. Physics from the numerical model followed the qualitative observations made using photometry of asperities along the interface, which visualized asperities in the 'locked' region -- larger asperities remained stuck throughout the loading cycle and the light transmitted through individual asperities decreased from the periphery as shear loads increased. The numerical partial slip, quantified by the potential energy stored by the asperity, increased relative to the normal pressure p. Asperity-asperity interactions were modeled along the interface using a quasi-static analysis. Progression of slip into the asperity field was increasingly inhibited as the normal confining force Fn was increased. The computational model provided an explanation as to why an increased confining force Fn could result in an increased resistance to slow rupture as well as an increased potential for larger foreshocks within the resistive, relatively 'locked' section of a fault. This study lays the foundation for more innovative laboratory work that could potentially improve the phenomenological models currently used to estimate earthquake hazard. (Abstract shortened by UMI.).

Active intra-basin faulting in the Northern Basin of Lake Malawi from seismic reflection data

NASA Astrophysics Data System (ADS)

Shillington, D. J.; Chindandali, P. R. N.; Scholz, C. A.; Ebinger, C. J.; Onyango, E. A.; Peterson, K.; Gaherty, J. B.; Nyblade, A.; Accardo, N. J.; McCartney, T.; Oliva, S. J.; Kamihanda, G.; Ferdinand, R.; Salima, J.; Mruma, A. H.

2016-12-01

Many questions remain about the development and evolution of fault systems in weakly extended rifts, including the relative roles of border faults and intra-basin faults, and segmentation at various scales. The northern Lake Malawi (Nyasa) rift in the East African Rift System is an early stage rift exhibiting pronounced tectonic segmentation, which is defined by 100-km-long border faults. The basins also contain a series of intrabasinal faults and associated synrift sediments. The occurrence of the 2009 Karonga Earthquake Sequence on one of these intrabasinal faults indicates that some of them are active. Here we present new multichannel seismic reflection data from the Northern Basin of the Malawi Rift collected in 2015 as a part of the SEGMeNT (Study of Extension and maGmatism in Malawi aNd Tanzania) project. This rift basin is bound on its east side by the west-dipping Livingstone border fault. Over 650 km of seismic reflection profiles were acquired in the Northern Basin using a 500 to 1540 cu in air gun array and a 1200- to 1500-m seismic streamer. Dip lines image a series of north-south oriented west-dipping intra-basin faults and basement reflections up to 5 s twtt near the border fault. Cumulative offsets on intra-basin faults decrease to the west. The largest intra-basin fault has a vertical displacement of >2 s two-way travel time, indicating that it has accommodated significant total extension. Some of these intra-basin faults offset the lake bottom and the youngest sediments by up to 50 s twtt ( 37 m), demonstrating they are still active. The two largest intra-basin faults exhibit the largest offsets of young sediments and also correspond to the area of highest seismicity based on analysis of seismic data from the 89-station SEGMeNT onshore/offshore network (see Peterson et al, this session). Fault patterns in MCS profiles vary along the basin, suggesting a smaller scale of segmentation of faults within the basin; these variations in fault patterns appear to correlate with variations in the distribution of aftershocks from the 2009 and 2014 Karonga earthquakes and in background seismicity beneath the lake, providing new constraints on length-displacement scaling for predictive models and earthquake hazards.

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

PubMed Central

Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

2015-01-01

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

Flux focusing eddy current probe

NASA Technical Reports Server (NTRS)

Simpson, John W. (Inventor); Clendenin, C. Gerald (Inventor); Fulton, James P. (Inventor); Wincheski, Russell A. (Inventor); Todhunter, Ronald G. (Inventor); Namkung, Min (Inventor); Nath, Shridhar C. (Inventor)

1997-01-01

A flux-focusing electromagnetic sensor which uses a ferromagnetic flux-focusing lens simplifies inspections and increases detectability of fatigue cracks and material loss in high conductivity material. The unique feature of the device is the ferrous shield isolating a high-turn pick-up coil from an excitation coil. The use of the magnetic shield is shown to produce a null voltage output across the receiving coil in the presence of an unflawed sample. A redistribution of the current flow in the sample caused by the presence of flaws, however, eliminates the shielding condition and a large output voltage is produced, yielding a clear unambiguous flaw signal. The maximum sensor output is obtained when positioned symmetrically above the crack. Hence, by obtaining the position of the maximum sensor output, it is possible to track the fault and locate the area surrounding its tip. The accuracy of tip location is enhanced by two unique features of the sensor; a very high signal-to-noise ratio of the probe's output which results in an extremely smooth signal peak across the fault, and a rapidly decaying sensor output outside a small area surrounding the crack tip which enables the region for searching to be clearly defined. Under low frequency operation, material thinning due to corrosion damage causes an incomplete shielding of the pick-up coil. The low frequency output voltage of the probe is therefore a direct indicator of the thickness of the test sample.

Support vector machines-based fault diagnosis for turbo-pump rotor

NASA Astrophysics Data System (ADS)

Yuan, Sheng-Fa; Chu, Fu-Lei

2006-05-01

Most artificial intelligence methods used in fault diagnosis are based on empirical risk minimisation principle and have poor generalisation when fault samples are few. Support vector machines (SVM) is a new general machine-learning tool based on structural risk minimisation principle that exhibits good generalisation even when fault samples are few. Fault diagnosis based on SVM is discussed. Since basic SVM is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification of SVM named 'one to others' algorithm is presented to solve the multi-class recognition problems. It is a binary tree classifier composed of several two-class classifiers organised by fault priority, which is simple, and has little repeated training amount, and the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.

Fault tolerant attitude sensing and force feedback control for unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Jagadish, Chirag

Two aspects of an unmanned aerial vehicle are studied in this work. One is fault tolerant attitude determination and the other is to provide force feedback to the joy-stick of the UAV so as to prevent faulty inputs from the pilot. Determination of attitude plays an important role in control of aerial vehicles. One way of defining the attitude is through Euler angles. These angles can be determined based on the measurements of the projections of the gravity and earth magnetic fields on the three body axes of the vehicle. Attitude determination in unmanned aerial vehicles poses additional challenges due to limitations of space, payload, power and cost. Therefore it provides for almost no room for any bulky sensors or extra sensor hardware for backup and as such leaves no room for sensor fault issues either. In the face of these limitations, this study proposes a fault tolerant computing of Euler angles by utilizing multiple different computation methods, with each method utilizing a different subset of the available sensor measurement data. Twenty-five such methods have been presented in this document. The capability of computing the Euler angles in multiple ways provides a diversified redundancy required for fault tolerance. The proposed approach can identify certain sets of sensor failures and even separate the reference fields from the disturbances. A bank-to-turn maneuver of the NASA GTM UAV is used to demonstrate the fault tolerance provided by the proposed method as well as to demonstrate the method of determining the correct Euler angles despite interferences by inertial acceleration disturbances. Attitude computation is essential for stability. But as of today most UAVs are commanded remotely by human pilots. While basic stability control is entrusted to machine or the on-board automatic controller, overall guidance is usually with humans. It is therefore the pilot who sets the command/references through a joy-stick. While this is a good compromise between complete automation and complete human control, it still poses some unique challenges. Pilots of manned aircraft are present inside the cockpit of the aircraft they fly and thus have a better feel of the flying environment and also the limitations of the flight. The same might not be true for UAV pilots stationed on the ground. A major handicap is that visual feedback is the only one available for the UAV pilot. An additional parameter like force feedback on the remote control joy-stick can help the UAV pilot to physically feel the limitation of the safe flight envelope. This can make the flying itself easier and safer. A method proposed here is to design a joy-stick assembly with an additional actuator. This actuator is controlled so as to generate a force feedback on the joy-stick. The control developed for this system is such that the actuator allows free movement for the pilot as long as the UAV is within the safe flight envelope. On the other hand, if it is outside this safe range, the actuator opposes the pilot's applied torque and prevents him/her from giving erroneous commands to the UAV.

Frictional, Hydraulic, and Acoustic Properties of Alpine Fault DFDP-1 Core

NASA Astrophysics Data System (ADS)

Carpenter, B. M.; Ikari, M.; Kitajima, H.; Kopf, A.; Marone, C.; Saffer, D. M.

2012-12-01

The Alpine Fault, a transpressional plate-boundary fault transecting the South Island of New Zealand, is the current focus of the Deep Fault Drilling Project (DFDP), a major fault zone drilling initiative. Phase 1 of this project included 2 boreholes that penetrated the active fault at depths of ˜100 m and ˜150 m, and provided a suite of core samples crossing the fault. Here, we report on laboratory measurements of frictional strength and constitutive behavior, permeability, and ultrasonic velocities for a suite of the recovered core samples We conducted friction experiments on powdered samples in a double-direct shear configuration at room temperature and humidity. Our results show that over a range of effective normal stresses from 10-100 MPa, friction coefficients are ~0.60-0.70, and are similar for all of the materials we tested. Rate-stepping tests document velocity-weakening behavior in the majority of wall rock samples, whereas the principal slip surface (PSS) and an adjacent clay-rich cataclasite exhibit velocity-strengthening behavior. We observe significant rates of frictional healing in all of our samples, indicating that that the fault easily regains its strength during interseismic periods. Our results indicate that seismic slip is not likely to nucleate in the clay-rich PSS at shallow depths, but might nucleate and propagate on the gouge/wall rock interface. We measured permeability using a constant head technique, on vertically oriented cylindrical mini-cores (i.e. ˜45 degrees to the plane of the Alpine Fault). We conducted these tests in a triaxial configuration, under isotropic stress conditions and effective confining pressures from ~2.5 - 63.5 MPa. We conducted ultrasonic wavespeed measurements concurrently with the permeability measurements to determine P- and S-wave velocities from time-of-flight. The permeability of all samples decreases systematically with increasing effective stress. The clay-rich cataclasite (1.37 x 10-19 m2) and PSS (1.62 x 10-20 m2) samples exhibit the lowest permeabilities. The cataclasite, and wall rock mylonite and gravel samples, all exhibit permeabilities > 10-18 m2. We also observe that permeability of the cataclasites appears to decrease with proximity to the active fault zone. Our laboratory measurements are consistent with borehole slug tests that show the fault is a hydraulic barrier, and suggest that fault rock permeability is sufficiently low to facilitate transient pore pressure effects during rapid slip, including thermal pressurization and dilatancy hardening. Elastic wave velocity increases systematically with increasing effective stress. We find the lowest P-wave velocities in clay-rich, poorly lithified samples from within and near the active fault, including hanging wall cataclasite, fault gouge, and footwall gravel. Our results are consistent with borehole logging data that show an increase in P-wave velocity from the mylonite into the competent cataclasites, and a decrease in P-wave velocity through the clay-rich cataclasite and into the fault zone.

Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites

PubMed Central

Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

2016-01-01

This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons. PMID:27092508

Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites.

PubMed

Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

2016-04-15

This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons.

Fuzzy model-based observers for fault detection in CSTR.

PubMed

Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

2015-11-01

Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Fault detection and isolation in the challenging Tennessee Eastman process by using image processing techniques.

PubMed

Hajihosseini, Payman; Anzehaee, Mohammad Mousavi; Behnam, Behzad

2018-05-22

The early fault detection and isolation in industrial systems is a critical factor in preventing equipment damage. In the proposed method, instead of using the time signals of sensors, the 2D image obtained by placing these signals next to each other in a matrix has been used; and then a novel fault detection and isolation procedure has been carried out based on image processing techniques. Different features including texture, wavelet transform, mean and standard deviation of the image accompanied with MLP and RBF neural networks based classifiers have been used for this purpose. Obtained results indicate the notable efficacy and success of the proposed method in detecting and isolating faults of the Tennessee Eastman benchmark process and its superiority over previous techniques. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

An Event-Based Approach to Distributed Diagnosis of Continuous Systems

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon

2010-01-01

Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

Hydraulic and acoustic properties of the active Alpine Fault, New Zealand: Laboratory measurements on DFDP-1 drill core

NASA Astrophysics Data System (ADS)

Carpenter, B. M.; Kitajima, H.; Sutherland, R.; Townend, J.; Toy, V. G.; Saffer, D. M.

2014-03-01

We report on laboratory measurements of permeability and elastic wavespeed for a suite of samples obtained by drilling across the active Alpine Fault on the South Island of New Zealand, as part of the first phase of the Deep Fault Drilling Project (DFDP-1). We find that clay-rich cataclasite and principal slip zone (PSZ) samples exhibit low permeabilities (⩽10-18 m), and that the permeability of hanging-wall cataclasites increases (from c. 10-18 m to 10-15 m) with distance from the fault. Additionally, the PSZ exhibits a markedly lower P-wave velocity and Young's modulus relative to the wall rocks. Our laboratory data are in good agreement with in situ wireline logging measurements and are consistent with the identification of an alteration zone surrounding the PSZ defined by observations of core samples. The properties of this zone and the low permeability of the PSZ likely govern transient hydrologic processes during earthquake slip, including thermal pressurization and dilatancy strengthening.

Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps.

PubMed

Moya, José M; Araujo, Alvaro; Banković, Zorana; de Goyeneche, Juan-Mariano; Vallejo, Juan Carlos; Malagón, Pedro; Villanueva, Daniel; Fraga, David; Romero, Elena; Blesa, Javier

2009-01-01

The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.

Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps

PubMed Central

Moya, José M.; Araujo, Álvaro; Banković, Zorana; de Goyeneche, Juan-Mariano; Vallejo, Juan Carlos; Malagón, Pedro; Villanueva, Daniel; Fraga, David; Romero, Elena; Blesa, Javier

2009-01-01

The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals. PMID:22291569

Role of the offshore Pedro Banks left-lateral strike-slip fault zone in the plate tectonic evolution of the northern Caribbean

NASA Astrophysics Data System (ADS)

Ott, B.; Mann, P.; Saunders, M.

2013-12-01

Previous workers, mainly mapping onland active faults on Caribbean islands, defined the northern Caribbean plate boundary zone as a 200-km-wide bounded by two active and parallel strike-slip faults: the Oriente fault along the northern edge of the Cayman trough with a GPS rate of 14 mm/yr, and and the Enriquillo-Plaintain Garden fault zone (EPGFZ) with a rate of 5-7 mm/yr. In this study we use 5,000 km of industry and academic data from the Nicaraguan Rise south and southwest of the EPGFZ in the maritime areas of Jamaica, Honduras, and Colombia to define an offshore, 700-km-long, active, left-lateral strike-slip fault in what has previously been considered the stable interior of the Caribbean plate as determined from plate-wide GPS studies. The fault was named by previous workers as the Pedro Banks fault zone because a 100-km-long segment of the fault forms an escarpment along the Pedro carbonate bank of the Nicaraguan Rise. Two fault segments of the PBFZ are defined: the 400-km-long eastern segment that exhibits large negative flower structures 10-50 km in width, with faults segments rupturing the sea floor as defined by high resolution 2D seismic data, and a 300-km-long western segment that is defined by a narrow zone of anomalous seismicity first observed by previous workers. The western end of the PBFZ terminates on a Quaternary rift structure, the San Andres rift, associated with Plio-Pleistocene volcanism and thickening trends indicating initial rifting in the Late Miocene. The southern end of the San Andreas rift terminates on the western Hess fault which also exhibits active strands consistent with left-lateral, strike-slip faults. The total length of the PBFZ-San Andres rift-Southern Hess escarpment fault is 1,200 km and traverses the entire western end of the Caribbean plate. Our interpretation is similar to previous models that have proposed the "stable" western Caribbean plate is broken by this fault whose rate of displacement is less than the threshold recognizable from the current GPS network (~3 mm/yr). The Late Miocene age of the fault indicates it may have activated during the Late Miocene to recent Hispaniola-Bahamas oblique collision event.

Gas Path On-line Fault Diagnostics Using a Nonlinear Integrated Model for Gas Turbine Engines

NASA Astrophysics Data System (ADS)

Lu, Feng; Huang, Jin-quan; Ji, Chun-sheng; Zhang, Dong-dong; Jiao, Hua-bin

2014-08-01

Gas turbine engine gas path fault diagnosis is closely related technology that assists operators in managing the engine units. However, the performance gradual degradation is inevitable due to the usage, and it result in the model mismatch and then misdiagnosis by the popular model-based approach. In this paper, an on-line integrated architecture based on nonlinear model is developed for gas turbine engine anomaly detection and fault diagnosis over the course of the engine's life. These two engine models have different performance parameter update rate. One is the nonlinear real-time adaptive performance model with the spherical square-root unscented Kalman filter (SSR-UKF) producing performance estimates, and the other is a nonlinear baseline model for the measurement estimates. The fault detection and diagnosis logic is designed to discriminate sensor fault and component fault. This integration architecture is not only aware of long-term engine health degradation but also effective to detect gas path performance anomaly shifts while the engine continues to degrade. Compared to the existing architecture, the proposed approach has its benefit investigated in the experiment and analysis.

Seismic measurements of the internal properties of fault zones

USGS Publications Warehouse

Mooney, W.D.; Ginzburg, A.

1986-01-01

The internal properties within and adjacent to fault zones are reviewed, principally on the basis of laboratory, borehole, and seismic refraction and reflection data. The deformation of rocks by faulting ranges from intragrain microcracking to severe alteration. Saturated microcracked and mildly fractured rocks do not exhibit a significant reduction in velocity, but, from borehole measurements, densely fractured rocks do show significantly reduced velocities, the amount of reduction generally proportional to the fracture density. Highly fractured rock and thick fault gouge along the creeping portion of the San Andreas fault are evidenced by a pronounced seismic low-velocity zone (LVZ), which is either very thin or absent along locked portions of the fault. Thus there is a correlation between fault slip behavior and seismic velocity structure within the fault zone; high pore pressure within the pronounced LVZ may be conductive to fault creep. Deep seismic reflection data indicate that crustal faults sometimes extend through the entire crust. Models of these data and geologic evidence are consistent with a composition of deep faults consisting of highly foliated, seismically anisotropic mylonites. ?? 1986 Birkha??user Verlag, Basel.

Potential of electrical resistivity tomography and muon density imaging to study spatio-temporal variations in the sub-surface

NASA Astrophysics Data System (ADS)

Lesparre, Nolwenn; Cabrera, Justo; Courbet, Christelle

2015-04-01

We explore the capacity of electrical resistivity tomography and muon density imaging to detect spatio-temporal variations of the medium surrounding a regional fault crossing the underground platform of Tournemire (Aveyron, France). The studied Cernon fault is sub-vertical and intersects perpendicularly the tunnel of Tournemire and extends to surface. The fault separates clay and limestones layers of the Dogger from limestones layers of the Lias. The Cernon fault presents a thickness of a ten of meters and drives water from an aquifer circulating at the top of the Dogger clay layer to the tunnel. An experiment combining electrical resistivity imaging and muon density imaging was setup taking advantage of the tunnel presence. A specific array of electrodes were set up, adapted for the characterization of the fault. Electrodes were placed along the tunnel as well as at the surface above the tunnel on both sides of the fault in order to acquire data in transmission across the massif to better cover the sounded medium. Electrical resistivity is particularly sensitive to water presence in the medium and thus carry information on the main water flow paths and on the pore space saturation. At the same time a muon sensor was placed in the tunnel under the fault region to detect muons coming from the sky after their crossing of the rock medium. Since the muon flux is attenuated as function of the quantity of matter crossed, muons flux measurements supply information on the medium average density along muons paths. The sensor presents 961 angles of view so measurements performed from one station allows a comparison of the muon flux temporal variations along the fault as well as in the medium surrounding the fault. As the water saturation of the porous medium fluctuates through time the medium density might indeed present sensible variations as shown by gravimetric studies. During the experiment important rainfalls occurred leading variations of the medium properties affecting density and electrical resistivity physical parameters. We show with data sets acquired before and after an important rainfall event how muon density and electrical resistivity imaging may complementary characterize variations of the medium properties. The development of such innovative experiments for hydrogeophysical studies presents then the ability to supply new information on fluid dynamics in the sub-surface.

Shock Prevention

NASA Technical Reports Server (NTRS)

1978-01-01

The electrician pictured is installing a General Electric Ground Fault Interrupter (GFI), a device which provides protection against electrical shock in the home or in industrial facilities. Shocks due to defective wiring in home appliances or other electrical equipment can cause severe burns, even death. As a result, the National Electrical Code now requires GFIs in all new homes constructed. This particular type of GFI employs a sensing element which derives from technology acquired in space projects by SCI Systems, Inc., Huntsville, Alabama, producer of sensors for GE and other manufacturers of GFI equipment. The sensor is based on the company's experience in developing miniaturized circuitry for space telemetry and other spacecraft electrical systems; this experience enabled SCI to package interruptor circuitry in the extremely limited space available and to produce sensory devices at practicable cost. The tiny sensor measures the strength of the electrical current and detects current differentials that indicate a fault in the functioning of an electrical system. The sensing element then triggers a signal to a disconnect mechanism in the GFI, which cuts off the current in the faulty circuit.

FDI and Accommodation Using NN Based Techniques

NASA Astrophysics Data System (ADS)

Garcia, Ramon Ferreiro; de Miguel Catoira, Alberto; Sanz, Beatriz Ferreiro

Massive application of dynamic backpropagation neural networks is used on closed loop control FDI (fault detection and isolation) tasks. The process dynamics is mapped by means of a trained backpropagation NN to be applied on residual generation. Process supervision is then applied to discriminate faults on process sensors, and process plant parameters. A rule based expert system is used to implement the decision making task and the corresponding solution in terms of faults accommodation and/or reconfiguration. Results show an efficient and robust FDI system which could be used as the core of an SCADA or alternatively as a complement supervision tool operating in parallel with the SCADA when applied on a heat exchanger.

On-Board Real-Time State and Fault Identification for Rovers

NASA Technical Reports Server (NTRS)

Washington, Richard

2000-01-01

For extended autonomous operation, rovers must identify potential faults to determine whether its execution needs to be halted or not. At the same time, rovers present particular challenges for state estimation techniques: they are subject to environmental influences that affect senior readings during normal and anomalous operation, and the sensors fluctuate rapidly both because of noise and because of the dynamics of the rover's interaction with its environment. This paper presents MAKSI, an on-board method for state estimation and fault diagnosis that is particularly appropriate for rovers. The method is based on a combination of continuous state estimation, wing Kalman filters, and discrete state estimation, wing a Markov-model representation.

An Integrated Approach for Aircraft Engine Performance Estimation and Fault Diagnostics

NASA Technical Reports Server (NTRS)

imon, Donald L.; Armstrong, Jeffrey B.

2012-01-01

A Kalman filter-based approach for integrated on-line aircraft engine performance estimation and gas path fault diagnostics is presented. This technique is specifically designed for underdetermined estimation problems where there are more unknown system parameters representing deterioration and faults than available sensor measurements. A previously developed methodology is applied to optimally design a Kalman filter to estimate a vector of tuning parameters, appropriately sized to enable estimation. The estimated tuning parameters can then be transformed into a larger vector of health parameters representing system performance deterioration and fault effects. The results of this study show that basing fault isolation decisions solely on the estimated health parameter vector does not provide ideal results. Furthermore, expanding the number of the health parameters to address additional gas path faults causes a decrease in the estimation accuracy of those health parameters representative of turbomachinery performance deterioration. However, improved fault isolation performance is demonstrated through direct analysis of the estimated tuning parameters produced by the Kalman filter. This was found to provide equivalent or superior accuracy compared to the conventional fault isolation approach based on the analysis of sensed engine outputs, while simplifying online implementation requirements. Results from the application of these techniques to an aircraft engine simulation are presented and discussed.

Availability Issues in Wireless Visual Sensor Networks

PubMed Central

Costa, Daniel G.; Silva, Ivanovitch; Guedes, Luiz Affonso; Vasques, Francisco; Portugal, Paulo

2014-01-01

Wireless visual sensor networks have been considered for a large set of monitoring applications related with surveillance, tracking and multipurpose visual monitoring. When sensors are deployed over a monitored field, permanent faults may happen during the network lifetime, reducing the monitoring quality or rendering parts or the entire network unavailable. In a different way from scalar sensor networks, camera-enabled sensors collect information following a directional sensing model, which changes the notions of vicinity and redundancy. Moreover, visual source nodes may have different relevancies for the applications, according to the monitoring requirements and cameras' poses. In this paper we discuss the most relevant availability issues related to wireless visual sensor networks, addressing availability evaluation and enhancement. Such discussions are valuable when designing, deploying and managing wireless visual sensor networks, bringing significant contributions to these networks. PMID:24526301

Spreading rate dependence of gravity anomalies along oceanic transform faults.

PubMed

Gregg, Patricia M; Lin, Jian; Behn, Mark D; Montési, Laurent G J

2007-07-12

Mid-ocean ridge morphology and crustal accretion are known to depend on the spreading rate of the ridge. Slow-spreading mid-ocean-ridge segments exhibit significant crustal thinning towards transform and non-transform offsets, which is thought to arise from a three-dimensional process of buoyant mantle upwelling and melt migration focused beneath the centres of ridge segments. In contrast, fast-spreading mid-ocean ridges are characterized by smaller, segment-scale variations in crustal thickness, which reflect more uniform mantle upwelling beneath the ridge axis. Here we present a systematic study of the residual mantle Bouguer gravity anomaly of 19 oceanic transform faults that reveals a strong correlation between gravity signature and spreading rate. Previous studies have shown that slow-slipping transform faults are marked by more positive gravity anomalies than their adjacent ridge segments, but our analysis reveals that intermediate and fast-slipping transform faults exhibit more negative gravity anomalies than their adjacent ridge segments. This finding indicates that there is a mass deficit at intermediate- and fast-slipping transform faults, which could reflect increased rock porosity, serpentinization of mantle peridotite, and/or crustal thickening. The most negative anomalies correspond to topographic highs flanking the transform faults, rather than to transform troughs (where deformation is probably focused and porosity and alteration are expected to be greatest), indicating that crustal thickening could be an important contributor to the negative gravity anomalies observed. This finding in turn suggests that three-dimensional magma accretion may occur near intermediate- and fast-slipping transform faults.

Security Police Career Ladders AFSCs 811X0, 811X2, and 811X2A.

DTIC Science & Technology

1984-11-01

MONITORS (GRP658) PERCENT MEMBERS PERFORMING TASKS (N=186) J424 PERFORM SPCDS OPERATOR REACTIONS TO SENSOR ALARM, LINE FAULT, OR UNIQUE LINE FAULT...MESSAGES 96 J426 PERFORM SPCDS VERIFICATION PROCEDURES 96 J423 PERFORM SMALL PERMANENT COMMUNICATIONS DISPLAY SEGMENT ( SPCDS ) SHUT-DOWN PROCEDURES 92 J425...PERFORM SPCDS START-UP PROCEDURES 91 J419 PERFORM BISS OPERATOR REACTION TO PRIME POWER LOSS OR SEVERE WEATHER WARNINGS 91 E192 MAKE ENTRIES ON AF

Fault Tolerant Computer Network Study

DTIC Science & Technology

1980-04-01

2. 1.2. 2 Air Data The air data function processes air pressures, temperature , and angle- of-attack measurements, and provides calibrated airspeed...attitude direction indicator. 2.1.5.2 Fixtaking Sensors used for fixtaking include the radar (in ground map mode), head- up display (for visual...VFR interdiction mission. The radar (ground map mode) is also the primary sensor at night and in adverse weather if the target presents a

Systematic Sensor Selection Strategy (S4) User Guide

NASA Technical Reports Server (NTRS)

Sowers, T. Shane

2012-01-01

This paper describes a User Guide for the Systematic Sensor Selection Strategy (S4). S4 was developed to optimally select a sensor suite from a larger pool of candidate sensors based on their performance in a diagnostic system. For aerospace systems, selecting the proper sensors is important for ensuring adequate measurement coverage to satisfy operational, maintenance, performance, and system diagnostic criteria. S4 optimizes the selection of sensors based on the system fault diagnostic approach while taking conflicting objectives such as cost, weight and reliability into consideration. S4 can be described as a general architecture structured to accommodate application-specific components and requirements. It performs combinational optimization with a user defined merit or cost function to identify optimum or near-optimum sensor suite solutions. The S4 User Guide describes the sensor selection procedure and presents an example problem using an open source turbofan engine simulation to demonstrate its application.

Cathodoluminescence of stacking fault bound excitons for local probing of the exciton diffusion length in single GaN nanowires

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nogues, Gilles, E-mail: gilles.nogues@neel.cnrs.fr; Den Hertog, Martien; Inst. NEEL, CNRS, F-38042 Grenoble

We perform correlated studies of individual GaN nanowires in scanning electron microscopy combined to low temperature cathodoluminescence, microphotoluminescence, and scanning transmission electron microscopy. We show that some nanowires exhibit well localized regions emitting light at the energy of a stacking fault bound exciton (3.42 eV) and are able to observe the presence of a single stacking fault in these regions. Precise measurements of the cathodoluminescence signal in the vicinity of the stacking fault give access to the exciton diffusion length near this location.

Negative Selection Algorithm for Aircraft Fault Detection

NASA Technical Reports Server (NTRS)

Dasgupta, D.; KrishnaKumar, K.; Wong, D.; Berry, M.

2004-01-01

We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.

Surface faulting along the inland Itozawa normal fault (eastern Japan) and relation to the 2011 Tohoku-oki megathrust earthquake

NASA Astrophysics Data System (ADS)

Ferry, Matthieu; Tsutsumi, Hiroyuki; Meghraoui, Mustapha; Toda, Shinji

2013-04-01

The 11 March 2011 Mw 9 Tohoku-oki earthquake ruptured ~500 km length of the Japan Trench along the coast of eastern Japan and significantly impacted the stress regime within the crust. The resulting change in seismicity over the Japan mainland was exhibited by the 11 April 2011 Mw 6.6 Iwaki earthquake that ruptured the Itozawa and Yunodake faults. Trending NNW and NW, respectively, these 70-80° W-dipping faults bound the Iwaki basin of Neogene age and have been reactivated simultaneously both along 15-km-long sections. Here, we present initial results from a paleoseismic excavation performed across the Itozawa fault within the Tsunagi Valley at the northern third of the observed surface rupture. At the Tsunagi site, the rupture affects a rice paddy, which provides an ideally horizontal initial state to collect detailed and accurate measurements. The surface break is composed of a continuous 30-to-40-cm-wide purely extensional crack that separates the uplifted block from a gently dipping 1-to-2-m-wide strip affected by right-stepping en-echelon cracks and locally bounded by a ~0.1-m-high reverse scarplet. Total station across-fault topographic profiles indicate the pre-earthquake ground surface was vertically deformed by ~0.6 m while direct field examinations reveal that well-defined rice paddy limits have been left-laterally offset by ~0.1 m. The 12-m-long, 3.5-m-deep trench exposes the 30-to-40-cm-thick cultivated soil overlaying a 1-m-thick red to yellow silt unit, a 2-m-thick alluvial gravel unit and a basal 0.1-1-m-thick organic-rich silt unit. Deformation associated to the 2011 rupture illustrates down-dip movement along a near-vertical fault with a well-expressed bending moment at the surface and generalized warping. On the north wall, the intermediate gravel unit displays a deformation pattern similar to granular flow with only minor discrete faulting and no splay to be continuously followed from the main fault to the surface. On the south wall, warping dominates as well but with some strain localization along two major splays that exhibit 15-20 cm of vertical offset. On both walls, the basal silt unit is vertically deformed by ~0.6 m, similarly to what is observed for the 2011 rupture. Furthermore, the base of said silt unit exhibits indication for secondary faulting prior to the 2011 event and that resemble cracks observed at the present-day surface. This suggests that the Itozawa fault has already ruptured in a similar fashion in the late Pleistocene). Hence, recent activity of the Itozawa fault may be controlled entirely by large to giant earthquakes along the Japan Trench.

Optimizing the Reliability and Performance of Service Composition Applications with Fault Tolerance in Wireless Sensor Networks

PubMed Central

Wu, Zhao; Xiong, Naixue; Huang, Yannong; Xu, Degang; Hu, Chunyang

2015-01-01

The services composition technology provides flexible methods for building service composition applications (SCAs) in wireless sensor networks (WSNs). The high reliability and high performance of SCAs help services composition technology promote the practical application of WSNs. The optimization methods for reliability and performance used for traditional software systems are mostly based on the instantiations of software components, which are inapplicable and inefficient in the ever-changing SCAs in WSNs. In this paper, we consider the SCAs with fault tolerance in WSNs. Based on a Universal Generating Function (UGF) we propose a reliability and performance model of SCAs in WSNs, which generalizes a redundancy optimization problem to a multi-state system. Based on this model, an efficient optimization algorithm for reliability and performance of SCAs in WSNs is developed based on a Genetic Algorithm (GA) to find the optimal structure of SCAs with fault-tolerance in WSNs. In order to examine the feasibility of our algorithm, we have evaluated the performance. Furthermore, the interrelationships between the reliability, performance and cost are investigated. In addition, a distinct approach to determine the most suitable parameters in the suggested algorithm is proposed. PMID:26561818

Trackside acoustic diagnosis of axle box bearing based on kurtosis-optimization wavelet denoising

NASA Astrophysics Data System (ADS)

Peng, Chaoyong; Gao, Xiaorong; Peng, Jianping; Wang, Ai

2018-04-01

As one of the key components of railway vehicles, the operation condition of the axle box bearing has a significant effect on traffic safety. The acoustic diagnosis is more suitable than vibration diagnosis for trackside monitoring. The acoustic signal generated by the train axle box bearing is an amplitude modulation and frequency modulation signal with complex train running noise. Although empirical mode decomposition (EMD) and some improved time-frequency algorithms have proved to be useful in bearing vibration signal processing, it is hard to extract the bearing fault signal from serious trackside acoustic background noises by using those algorithms. Therefore, a kurtosis-optimization-based wavelet packet (KWP) denoising algorithm is proposed, as the kurtosis is the key indicator of bearing fault signal in time domain. Firstly, the geometry based Doppler correction is applied to signals of each sensor, and with the signal superposition of multiple sensors, random noises and impulse noises, which are the interference of the kurtosis indicator, are suppressed. Then, the KWP is conducted. At last, the EMD and Hilbert transform is applied to extract the fault feature. Experiment results indicate that the proposed method consisting of KWP and EMD is superior to the EMD.

Life support subsystem monitoring instrumentation

NASA Technical Reports Server (NTRS)

Powell, J. D.; Kostell, G. D.

1974-01-01

The recognition of the need for instrumentation in manned spacecraft life-support subsystems has increased significantly over the past several years. Of the required control and monitoring instrumentation, this paper will focus on the monitoring instrumentation as applied to life-support subsystems. The initial approach used independent sensors, independent sensor signal conditioning circuitry, and independent logic circuitry to provide shutdown protection only. This monitoring system was replaced with a coordinated series of printed circuit cards, each of which contains all the electronics to service one sensor and provide performance trend information, fault detection and isolation information, and shutdown protection. Finally, a review of sensor and instrumentation problems is presented, and the requirement for sensors with built-in signal conditioning and provisions for in situ calibration is discussed.

A novel redundant INS based on triple rotary inertial measurement units

NASA Astrophysics Data System (ADS)

Chen, Gang; Li, Kui; Wang, Wei; Li, Peng

2016-10-01

Accuracy and reliability are two key performances of inertial navigation system (INS). Rotation modulation (RM) can attenuate the bias of inertial sensors and make it possible for INS to achieve higher navigation accuracy with lower-class sensors. Therefore, the conflict between the accuracy and cost of INS can be eased. Traditional system redundancy and recently researched sensor redundancy are two primary means to improve the reliability of INS. However, how to make the best use of the redundant information from redundant sensors hasn’t been studied adequately, especially in rotational INS. This paper proposed a novel triple rotary unit strapdown inertial navigation system (TRUSINS), which combines RM and sensor redundancy design to enhance the accuracy and reliability of rotational INS. Each rotary unit independently rotates to modulate the errors of two gyros and two accelerometers. Three units can provide double sets of measurements along all three axes of body frame to constitute a couple of INSs which make TRUSINS redundant. Experiments and simulations based on a prototype which is made up of six fiber-optic gyros with drift stability of 0.05° h-1 show that TRUSINS can achieve positioning accuracy of about 0.256 n mile h-1, which is ten times better than that of a normal non-rotational INS with the same level inertial sensors. The theoretical analysis and the experimental results show that due to the advantage of the innovative structure, the designed fault detection and isolation (FDI) strategy can tolerate six sensor faults at most, and is proved to be effective and practical. Therefore, TRUSINS is particularly suitable and highly beneficial for the applications where high accuracy and high reliability is required.

The emergence of asymmetric normal fault systems under symmetric boundary conditions

NASA Astrophysics Data System (ADS)

Schöpfer, Martin P. J.; Childs, Conrad; Manzocchi, Tom; Walsh, John J.; Nicol, Andrew; Grasemann, Bernhard

2017-11-01

Many normal fault systems and, on a smaller scale, fracture boudinage often exhibit asymmetry with one fault dip direction dominating. It is a common belief that the formation of domino and shear band boudinage with a monoclinic symmetry requires a component of layer parallel shearing. Moreover, domains of parallel faults are frequently used to infer the presence of a décollement. Using Distinct Element Method (DEM) modelling we show, that asymmetric fault systems can emerge under symmetric boundary conditions. A statistical analysis of DEM models suggests that the fault dip directions and system polarities can be explained using a random process if the strength contrast between the brittle layer and the surrounding material is high. The models indicate that domino and shear band boudinage are unreliable shear-sense indicators. Moreover, the presence of a décollement should not be inferred on the basis of a domain of parallel faults alone.

Layered clustering multi-fault diagnosis for hydraulic piston pump

NASA Astrophysics Data System (ADS)

Du, Jun; Wang, Shaoping; Zhang, Haiyan

2013-04-01

Efficient diagnosis is very important for improving reliability and performance of aircraft hydraulic piston pump, and it is one of the key technologies in prognostic and health management system. In practice, due to harsh working environment and heavy working loads, multiple faults of an aircraft hydraulic pump may occur simultaneously after long time operations. However, most existing diagnosis methods can only distinguish pump faults that occur individually. Therefore, new method needs to be developed to realize effective diagnosis of simultaneous multiple faults on aircraft hydraulic pump. In this paper, a new method based on the layered clustering algorithm is proposed to diagnose multiple faults of an aircraft hydraulic pump that occur simultaneously. The intensive failure mechanism analyses of the five main types of faults are carried out, and based on these analyses the optimal combination and layout of diagnostic sensors is attained. The three layered diagnosis reasoning engine is designed according to the faults' risk priority number and the characteristics of different fault feature extraction methods. The most serious failures are first distinguished with the individual signal processing. To the desultory faults, i.e., swash plate eccentricity and incremental clearance increases between piston and slipper, the clustering diagnosis algorithm based on the statistical average relative power difference (ARPD) is proposed. By effectively enhancing the fault features of these two faults, the ARPDs calculated from vibration signals are employed to complete the hypothesis testing. The ARPDs of the different faults follow different probability distributions. Compared with the classical fast Fourier transform-based spectrum diagnosis method, the experimental results demonstrate that the proposed algorithm can diagnose the multiple faults, which occur synchronously, with higher precision and reliability.

Progressive deformation of the Chugach accretionary complex, Alaska, during a paleogene ridge-trench encounter

USGS Publications Warehouse

Kusky, Timothy M.

1997-01-01

The Mesozoic accretionary wedge of south-central Alaska is cut by an array of faults including dextral and sinistral strike-slip faults, synthetic and antithetic thrust faults, and synthetic and antithetic normal faults. The three fault sets are characterized by quartz ± calcite ± chlorite ± prehnite slickensides, and are all relatively late, i.e. all truncate ductile fabrics of the host rocks. Cross-cutting relationships suggest that the thrust fault sets predate the late normal and strike-slip fault sets. Together, the normal and strike-slip fault system exhibits orthorhombic symmetry. Thrust faulting shortened the wedge subhorizontally perpendicular to strike, and then normal and strike-slip faulting extended the wedge oblique to orogenic strike. Strongly curved slickenlines on some faults of each set reveal that displacement directions changed over time. On dip-slip faults (thrust and normal), slickenlines tend to become steeper with younger increments of slip, whereas on strike-slip faults, slickenlines become shallower with younger strain increments. These patterns may result from progressive exhumation of the accretionary wedge while the faults were active, with the curvature of the slickenlines tracking the change from a non-Andersonian stress field at depth to a more Andersonian system (σ1 or σ2 nearly vertical) at shallower crustal levels.We interpret this complex fault array as a progressive deformation that is one response to Paleocene-Eocene subduction of the Kula-Farallon spreading center beneath the accretionary complex because: (1) on the Kenai Peninsula, ENE-striking dextral faults of this array exhibit mutually cross-cutting relationships with Paleocene-Eocene dikes related to ridge subduction; and (2) mineralized strike-slip and normal faults of the orthorhombic system have yielded 40Ar/39Ar ages identical to near-trench intrusives related to ridge subduction. Both features are diachronous along-strike, having formed at circa 65 Ma in the west and 50 Ma in the east. Exhumation of deeper levels of the southern Alaska accretionary wedge and formation of this late fault array is interpreted as a critical taper adjustment to subduction of progressively younger oceanic lithosphere yielding a shallower basal de´collement dip as the Kula-Farallon ridge approached the accretionary prism. The late structures also record different kinematic regimes associated with subduction of different oceanic plates, before and after ridge subduction. Prior to triple junction passage, subduction of the Farallon plate occurred at nearly right angles to the trench axis, whereas after triple junction migration, subduction of the Kula plate involved a significant component of dextral transpression and northward translation of the Chugach terrane. The changes in kinematics are apparent in the sequence of late structures from: (1) thrusting; (2) near-trench plutonism associated with normal + strike-slip faulting; (3) very late gouge-filled dextral faults.

Phase response curves for models of earthquake fault dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Franović, Igor, E-mail: franovic@ipb.ac.rs; Kostić, Srdjan; Perc, Matjaž

We systematically study effects of external perturbations on models describing earthquake fault dynamics. The latter are based on the framework of the Burridge-Knopoff spring-block system, including the cases of a simple mono-block fault, as well as the paradigmatic complex faults made up of two identical or distinct blocks. The blocks exhibit relaxation oscillations, which are representative for the stick-slip behavior typical for earthquake dynamics. Our analysis is carried out by determining the phase response curves of first and second order. For a mono-block fault, we consider the impact of a single and two successive pulse perturbations, further demonstrating how themore » profile of phase response curves depends on the fault parameters. For a homogeneous two-block fault, our focus is on the scenario where each of the blocks is influenced by a single pulse, whereas for heterogeneous faults, we analyze how the response of the system depends on whether the stimulus is applied to the block having a shorter or a longer oscillation period.« less

Flux-focusing eddy current probe and method for flaw detection

NASA Technical Reports Server (NTRS)

Simpson, John W. (Inventor); Clendenin, C. Gerald (Inventor)

1993-01-01

A flux-focusing electromagnetic sensor which uses a ferromagnetic flux-focusing lens simplifies inspections and increases detectability of fatigue cracks and material loss in high conductivity material is presented. The unique feature of the device is the ferrous shield isolating a high-turn pick-up coil from an excitation coil. The use of the magnetic shield is shown to produce a null voltage output across the receiving coil in the presence of an unflawed sample. A redistribution of the current flow in the sample caused by the presence of flaws, however, eliminates the shielding condition and a large output voltage is produced, yielding a clear unambiguous flaw signal. The maximum sensor output is obtained when positioned symmetrically above the crack. Hence, by obtaining the position of the maximum sensor output, it is possible to track the fault and locate the area surrounding its tip. The accuracy of tip location is enhanced by two unique features of the sensor; a very high signal-to-noise ratio of the probe's output which results in an extremely smooth signal peak across the fault, and a rapidly decaying sensor output outside a small area surrounding the crack tip which enables the region for searching to be clearly defined. Under low frequency operation, material thinning due to corrosion damage causes an incomplete shielding of the pick-up coil. The low frequency output voltage of the probe is therefore a direct indicator of the thickness of the test sample.

Sensor Data Qualification for Autonomous Operation of Space Systems

NASA Technical Reports Server (NTRS)

Maul, William A.; Melcher, Kevin J.; Chicatelli, Amy K.; Sowers, T. Shane

2006-01-01

NASA's new Exploration initiative for both robotic and manned missions will require higher levels of reliability, autonomy and reconfiguration capability to make the missions safe, successful and affordable. Future systems will require diagnostic reasoning to assess the health of the system in order to maintain the system s functionality. The diagnostic reasoning and assessment will involve data qualification, fault detection, fault isolation and remediation control. A team of researchers at the NASA Glenn Research Center is currently working on a Sensor Data Qualification (SDQ) system that will support these critical evaluation processes, for both automated and human-in-the-loop applications. Data qualification is required as a first step so that critical safety and operational decisions are based on good data. The SDQ system would monitor a network of related sensors to determine the health of individual sensors within that network. Various diagnostic systems such as the Caution and Warning System would then use the sensor health information with confidence. The proposed SDQ technology will be demonstrated on a variety of subsystems that are relevant to NASA s Exploration systems, which currently include an electrical power system and a cryogenic fluid management system. The focus of this paper is the development and demonstration of a SDQ application for a prototype power distribution unit that is representative of a Crew Exploration Vehicle electrical power system; this provides a unique and relevant environment in which to demonstrate the feasibility of the SDQ technology.

Vehicle Integrated Propulsion Research for the Study of Health Management Capabilities

NASA Technical Reports Server (NTRS)

Lekki, John D.; Simon, Donald L.; Hunter, Gary W.; Woike, Mary; Tokars, Roger P.

2012-01-01

Presentation on vehicle integrated propulsion research results and planning. This research emphasizes the testing of advanced health management sensors and diagnostics in an aircraft engine that is operated through multiple baseline and fault conditions.

Fault detection on a sewer network by a combination of a Kalman filter and a binary sequential probability ratio test

NASA Astrophysics Data System (ADS)

Piatyszek, E.; Voignier, P.; Graillot, D.

2000-05-01

One of the aims of sewer networks is the protection of population against floods and the reduction of pollution rejected to the receiving water during rainy events. To meet these goals, managers have to equip the sewer networks with and to set up real-time control systems. Unfortunately, a component fault (leading to intolerable behaviour of the system) or sensor fault (deteriorating the process view and disturbing the local automatism) makes the sewer network supervision delicate. In order to ensure an adequate flow management during rainy events it is essential to set up procedures capable of detecting and diagnosing these anomalies. This article introduces a real-time fault detection method, applicable to sewer networks, for the follow-up of rainy events. This method consists in comparing the sensor response with a forecast of this response. This forecast is provided by a model and more precisely by a state estimator: a Kalman filter. This Kalman filter provides not only a flow estimate but also an entity called 'innovation'. In order to detect abnormal operations within the network, this innovation is analysed with the binary sequential probability ratio test of Wald. Moreover, by crossing available information on several nodes of the network, a diagnosis of the detected anomalies is carried out. This method provided encouraging results during the analysis of several rains, on the sewer network of Seine-Saint-Denis County, France.

Does seismic activity control carbon exchanges between transform-faults in old ocean crust and the deep sea? A hypothesis examined by the EU COST network FLOWS

NASA Astrophysics Data System (ADS)

Lever, M. A.

2014-12-01

The European Cooperation in Science and Technology (COST)-Action FLOWS (http://www.cost.eu/domains_actions/essem/Actions/ES1301) was initiated on the 25th of October 2013. It is a consortium formed by members of currently 14 COST countries and external partners striving to better understand the interplay between earthquakes and fluid flow at transform-faults in old oceanic crust. The recent occurrence of large earthquakes and discovery of deep fluid seepage calls for a revision of the postulated hydrogeological inactivity and low seismic activity of old oceanic transform-type plate boundaries, and indicates that earthquakes and fluid flow are intrinsically associated. This Action merges the expertise of a large number of research groups and supports the development of multidisciplinary knowledge on how seep fluid (bio)chemistry relates to seismicity. It aims to identify (bio)geochemical proxies for the detection of precursory seismic signals and to develop innovative physico-chemical sensors for deep-ocean seismogenic faults. National efforts are coordinated through Working Groups (WGs) focused on 1) geophysical and (bio)geochemical data acquisition; 2) modelling of structure and seismicity of faults; 3) engineering of deep-ocean physico-chemical seismic sensors; and 4) integration and dissemination. This poster will illustrate the overarching goals of the FLOWS Group, with special focus to research goals concerning the role of seismic activity in controlling the release of carbon from the old ocean crust into the deep ocean.

Dynamical jumping real-time fault-tolerant routing protocol for wireless sensor networks.

PubMed

Wu, Guowei; Lin, Chi; Xia, Feng; Yao, Lin; Zhang, He; Liu, Bing

2010-01-01

In time-critical wireless sensor network (WSN) applications, a high degree of reliability is commonly required. A dynamical jumping real-time fault-tolerant routing protocol (DMRF) is proposed in this paper. Each node utilizes the remaining transmission time of the data packets and the state of the forwarding candidate node set to dynamically choose the next hop. Once node failure, network congestion or void region occurs, the transmission mode will switch to jumping transmission mode, which can reduce the transmission time delay, guaranteeing the data packets to be sent to the destination node within the specified time limit. By using feedback mechanism, each node dynamically adjusts the jumping probabilities to increase the ratio of successful transmission. Simulation results show that DMRF can not only efficiently reduce the effects of failure nodes, congestion and void region, but also yield higher ratio of successful transmission, smaller transmission delay and reduced number of control packets.

A study of the use of vibration and stress wave sensing for the detection of bearing failure

NASA Technical Reports Server (NTRS)

Ensor, L. C.; Feng, C. C.

1975-01-01

Results from an experimental study of vibrations and stress waves emitted from ball bearings are presented. Fatique tests were run with both high quality bearings and man faulted bearings, all of one size. Tests were instrumented with different sensors to detect the noises from 10 Hz to 1 MHz. Frequency spectrum plots are presented. The modulation characteristics of the ultrasonic noises were analyzed, and acoustic emission type measurements were conducted. Results are presented which show that there are usable acoustic signal levels even beyond 500 KHz. These signal levels are modulated by a low frequency carrier which is a function of the stress loading and acoustic transmissibility. The results were correlated to fault size in the bearings. The correlation shows that the sensor used for signals from 100 KHz to 1 MHz gave the best sensitivity and detected the generation of very small spalls or pits.

Standardized Fault-Tolerant Sensing Nodes for an Intelligent Turbine Engine Control System (Preprint)

DTIC Science & Technology

2013-05-01

representation of a centralized control system on a turbine engine. All actuators and sensors are point-to-point cabled to the controller ( FADEC ) which...electronics themselves. Figure 1: Centralized Control System Each function resides within the FADEC and uses Unique Point-to-Point Analog...distributed control system on the same turbine engine. The actuators and sensors interface to Smart Nodes which, in turn communicate to the FADEC via

Study on the non-contact FBG vibration sensor and its application

NASA Astrophysics Data System (ADS)

Li, Tianliang; Tan, Yuegang; Zhou, Zude; Cai, Li; Liu, Sai; He, Zhongting; Zheng, Kai

2015-06-01

A non-contact vibration sensor based on the fiber Bragg grating (FBG) sensor has been presented, and it is used to monitor the vibration of rotating shaft. In the paper, we describe the principle of the sensor and make some experimental analyses. The analysis results show that the sensitivity and linearity of the sensor are -1.5 pm/μm and 4.11% within a measuring range of 2 mm-2.6 mm, respectively. When it is used to monitor the vibration of the rotating shaft, the analysis signals of vibration of the rotating shaft and the critical speed of rotation obtained are the same as that obtained from the eddy current sensor. It verifies that the sensor can be used for the non-contact measurement of vibration of the rotating shaft system and for fault monitoring and diagnosis of rotating machinery.

Further Structural Intelligence for Sensors Cluster Technology in Manufacturing

PubMed Central

Mekid, Samir

2006-01-01

With the ever increasing complex sensing and actuating tasks in manufacturing plants, intelligent sensors cluster in hybrid networks becomes a rapidly expanding area. They play a dominant role in many fields from macro and micro scale. Global object control and the ability to self organize into fault-tolerant and scalable systems are expected for high level applications. In this paper, new structural concepts of intelligent sensors and networks with new intelligent agents are presented. Embedding new functionalities to dynamically manage cooperative agents for autonomous machines are interesting key enabling technologies most required in manufacturing for zero defects production.

Integrated rate isolation sensor

NASA Technical Reports Server (NTRS)

Brady, Tye (Inventor); Henderson, Timothy (Inventor); Phillips, Richard (Inventor); Zimpfer, Doug (Inventor); Crain, Tim (Inventor)

2012-01-01

In one embodiment, a system for providing fault-tolerant inertial measurement data includes a sensor for measuring an inertial parameter and a processor. The sensor has less accuracy than a typical inertial measurement unit (IMU). The processor detects whether a difference exists between a first data stream received from a first inertial measurement unit and a second data stream received from a second inertial measurement unit. Upon detecting a difference, the processor determines whether at least one of the first or second inertial measurement units has failed by comparing each of the first and second data streams to the inertial parameter.

A new fault diagnosis algorithm for AUV cooperative localization system

NASA Astrophysics Data System (ADS)

Shi, Hongyang; Miao, Zhiyong; Zhang, Yi

2017-10-01

Multiple AUVs cooperative localization as a new kind of underwater positioning technology, not only can improve the positioning accuracy, but also has many advantages the single AUV does not have. It is necessary to detect and isolate the fault to increase the reliability and availability of the AUVs cooperative localization system. In this paper, the Extended Multiple Model Adaptive Cubature Kalmam Filter (EMMACKF) method is presented to detect the fault. The sensor failures are simulated based on the off-line experimental data. Experimental results have shown that the faulty apparatus can be diagnosed effectively using the proposed method. Compared with Multiple Model Adaptive Extended Kalman Filter and Multi-Model Adaptive Unscented Kalman Filter, both accuracy and timelines have been improved to some extent.

Aftershocks to Philippine quake found within nearby megathrust fault

NASA Astrophysics Data System (ADS)

Schultz, Colin

2013-02-01

On 31 August 2012 a magnitude 7.6 earthquake ruptured deep beneath the sea floor of the Philippine Trench, a powerful intraplate earthquake centered seaward of the plate boundary. In the wake of the main shock, sensors detected a flurry of aftershocks, counting 110 in total. Drawing on seismic wave observations and rupture mechanisms calculated for the aftershocks, Ye et al. found that many were located near the epicenter of the main intraplate quake but at shallower depth; all involved normal faulting. Some shallow thrusting aftershocks were located farther to the west, centered within the potentially dangerous megathrust fault formed by the subduction of the Philippine Sea plate beneath the Philippine microplate, the piece of crust housing the Philippine Islands.

Geodetic exploration of strain along the El Pilar Fault in northeastern Venezuela

NASA Astrophysics Data System (ADS)

Reinoza, C.; Jouanne, F.; Audemard, F. A.; Schmitz, M.; Beck, C.

2015-03-01

We use Global Navigation Satellite Systems observations in northeastern Venezuela to constrain the El Pilar Fault (EPF) kinematics and to explore the effects of the variable elastic properties of the surrounding medium and of the fault geometry on inferred slip rates and locking depth. The velocity field exhibits an asymmetric velocity gradient on either side of the EPF. We use five different approaches to explore possible models to explain this asymmetry. First, we infer a 1.6 km locking depth using a classic elastic half-space dislocation model. Second, we infer a 1.5 km locking depth and a 0.33 asymmetry coefficient using a heterogeneous asymmetric model, including contrasting material properties on either side of a vertical fault, suggesting that the igneous-metamorphic terranes on the northern side are ~2 times more rigid than the sedimentary southern side. Third, we use a three-dimensional elastostatic model to evaluate the presence of a compliant zone, suggesting a 30% reduction of rigidity in the upper 3 km at the depth of a 1 to 5 km wide fault zone. Fourth, we evaluate the distribution of fault slip, revealing a widespread partial creep pattern in the eastern upper segment, while the upper western segment exhibits a partially locked area, which coincides with the rupture surface of the 1797 and 1929 earthquakes. To supplement these models, we upgrade the previously published displacement simulation method using nonvertical dislocations with data acquired between 2003 and 2013. The localized aseismic displacement pattern associated with creeping or partially creeping fault segments could explain the low level of historic seismicity.

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine

PubMed Central

Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin

2016-01-01

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox. PMID:26848665

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine.

PubMed

Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin

2016-02-02

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox.

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

NASA Astrophysics Data System (ADS)

Shao, Si-Yu; Sun, Wen-Jun; Yan, Ru-Qiang; Wang, Peng; Gao, Robert X.

2017-11-01

Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

HETDEX tracker control system design and implementation

NASA Astrophysics Data System (ADS)

Beno, Joseph H.; Hayes, Richard; Leck, Ron; Penney, Charles; Soukup, Ian

2012-09-01

To enable the Hobby-Eberly Telescope Dark Energy Experiment, The University of Texas at Austin Center for Electromechanics and McDonald Observatory developed a precision tracker and control system - an 18,000 kg robot to position a 3,100 kg payload within 10 microns of a desired dynamic track. Performance requirements to meet science needs and safety requirements that emerged from detailed Failure Modes and Effects Analysis resulted in a system of 13 precision controlled actuators and 100 additional analog and digital devices (primarily sensors and safety limit switches). Due to this complexity, demanding accuracy requirements, and stringent safety requirements, two independent control systems were developed. First, a versatile and easily configurable centralized control system that links with modeling and simulation tools during the hardware and software design process was deemed essential for normal operation including motion control. A second, parallel, control system, the Hardware Fault Controller (HFC) provides independent monitoring and fault control through a dedicated microcontroller to force a safe, controlled shutdown of the entire system in the event a fault is detected. Motion controls were developed in a Matlab-Simulink simulation environment, and coupled with dSPACE controller hardware. The dSPACE real-time operating system collects sensor information; motor commands are transmitted over a PROFIBUS network to servo amplifiers and drive motor status is received over the same network. To interface the dSPACE controller directly to absolute Heidenhain sensors with EnDat 2.2 protocol, a custom communication board was developed. This paper covers details of operational control software, the HFC, algorithms, tuning, debugging, testing, and lessons learned.

A Novel Permanent Magnetic Angular Acceleration Sensor

PubMed Central

Zhao, Hao; Feng, Hao

2015-01-01

Angular acceleration is an important parameter for status monitoring and fault diagnosis of rotary machinery. Therefore, we developed a novel permanent magnetic angular acceleration sensor, which is without rotation angle limitations and could directly measure the instantaneous angular acceleration of the rotating system. The sensor rotor only needs to be coaxially connected with the rotating system, which enables convenient sensor installation. For the cup structure of the sensor rotor, it has a relatively small rotational inertia. Due to the unique mechanical structure of the sensor, the output signal of the sensor can be directed without a slip ring, which avoids signal weakening effect. In this paper, the operating principle of the sensor is described, and simulated using finite element method. The sensitivity of the sensor is calibrated by torsional pendulum and angle sensor, yielding an experimental result of about 0.88 mV/(rad·s−2). Finally, the angular acceleration of the actual rotating system has been tested, using both a single-phase asynchronous motor and a step motor. Experimental result confirms the operating principle of the sensor and indicates that the sensor has good practicability. PMID:26151217

Results of Gravity Fieldwork Conducted in March 2008 in the Moapa Valley Region of Clark County, Nevada

USGS Publications Warehouse

Scheirer, Daniel S.; Andreasen, Arne Dossing

2008-01-01

In March 2008, we collected gravity data along 12 traverses across newly-mapped faults in the Moapa Valley region of Clark County, Nevada. In areas crossed by these faults, the traverses provide better definition of the gravity field and, thus, the density structure, than prior gravity observations. Access problems prohibited complete gravity coverage along all of the planned gravity traverses, and we added and adjusted the locations of traverses to maximize our data collection. Most of the traverses exhibit isostatic gravity anomalies that have gradients characteristic of exposed or buried faults, including several of the newly-mapped faults.

Learning in the model space for cognitive fault diagnosis.

PubMed

Chen, Huanhuan; Tino, Peter; Rodan, Ali; Yao, Xin

2014-01-01

The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework.

Remote Structural Health Monitoring and Advanced Prognostics of Wind Turbines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Douglas Brown; Bernard Laskowski

The prospect of substantial investment in wind energy generation represents a significant capital investment strategy. In order to maximize the life-cycle of wind turbines, associated rotors, gears, and structural towers, a capability to detect and predict (prognostics) the onset of mechanical faults at a sufficiently early stage for maintenance actions to be planned would significantly reduce both maintenance and operational costs. Advancement towards this effort has been made through the development of anomaly detection, fault detection and fault diagnosis routines to identify selected fault modes of a wind turbine based on available sensor data preceding an unscheduled emergency shutdown. Themore » anomaly detection approach employs spectral techniques to find an approximation of the data using a combination of attributes that capture the bulk of variability in the data. Fault detection and diagnosis (FDD) is performed using a neural network-based classifier trained from baseline and fault data recorded during known failure conditions. The approach has been evaluated for known baseline conditions and three selected failure modes: pitch rate failure, low oil pressure failure and a gearbox gear-tooth failure. Experimental results demonstrate the approach can distinguish between these failure modes and normal baseline behavior within a specified statistical accuracy.« less

Fast and accurate spectral estimation for online detection of partial broken bar in induction motors

NASA Astrophysics Data System (ADS)

Samanta, Anik Kumar; Naha, Arunava; Routray, Aurobinda; Deb, Alok Kanti

2018-01-01

In this paper, an online and real-time system is presented for detecting partial broken rotor bar (BRB) of inverter-fed squirrel cage induction motors under light load condition. This system with minor modifications can detect any fault that affects the stator current. A fast and accurate spectral estimator based on the theory of Rayleigh quotient is proposed for detecting the spectral signature of BRB. The proposed spectral estimator can precisely determine the relative amplitude of fault sidebands and has low complexity compared to available high-resolution subspace-based spectral estimators. Detection of low-amplitude fault components has been improved by removing the high-amplitude fundamental frequency using an extended-Kalman based signal conditioner. Slip is estimated from the stator current spectrum for accurate localization of the fault component. Complexity and cost of sensors are minimal as only a single-phase stator current is required. The hardware implementation has been carried out on an Intel i7 based embedded target ported through the Simulink Real-Time. Evaluation of threshold and detectability of faults with different conditions of load and fault severity are carried out with empirical cumulative distribution function.

Robust dead reckoning system for mobile robots based on particle filter and raw range scan.

PubMed

Duan, Zhuohua; Cai, Zixing; Min, Huaqing

2014-09-04

Robust dead reckoning is a complicated problem for wheeled mobile robots (WMRs), where the robots are faulty, such as the sticking of sensors or the slippage of wheels, for the discrete fault models and the continuous states have to be estimated simultaneously to reach a reliable fault diagnosis and accurate dead reckoning. Particle filters are one of the most promising approaches to handle hybrid system estimation problems, and they have also been widely used in many WMRs applications, such as pose tracking, SLAM, video tracking, fault identification, etc. In this paper, the readings of a laser range finder, which may be also interfered with by noises, are used to reach accurate dead reckoning. The main contribution is that a systematic method to implement fault diagnosis and dead reckoning in a particle filter framework concurrently is proposed. Firstly, the perception model of a laser range finder is given, where the raw scan may be faulty. Secondly, the kinematics of the normal model and different fault models for WMRs are given. Thirdly, the particle filter for fault diagnosis and dead reckoning is discussed. At last, experiments and analyses are reported to show the accuracy and efficiency of the presented method.

Robust Dead Reckoning System for Mobile Robots Based on Particle Filter and Raw Range Scan

PubMed Central

Duan, Zhuohua; Cai, Zixing; Min, Huaqing

2014-01-01

Robust dead reckoning is a complicated problem for wheeled mobile robots (WMRs), where the robots are faulty, such as the sticking of sensors or the slippage of wheels, for the discrete fault models and the continuous states have to be estimated simultaneously to reach a reliable fault diagnosis and accurate dead reckoning. Particle filters are one of the most promising approaches to handle hybrid system estimation problems, and they have also been widely used in many WMRs applications, such as pose tracking, SLAM, video tracking, fault identification, etc. In this paper, the readings of a laser range finder, which may be also interfered with by noises, are used to reach accurate dead reckoning. The main contribution is that a systematic method to implement fault diagnosis and dead reckoning in a particle filter framework concurrently is proposed. Firstly, the perception model of a laser range finder is given, where the raw scan may be faulty. Secondly, the kinematics of the normal model and different fault models for WMRs are given. Thirdly, the particle filter for fault diagnosis and dead reckoning is discussed. At last, experiments and analyses are reported to show the accuracy and efficiency of the presented method. PMID:25192318

Crystal plastic earthquakes in dolostones

NASA Astrophysics Data System (ADS)

Passelegue, Francois; Aubry, Jerome; Nicolas, Aurelien; Fondriest, Michele; Schubnel, Alexandre; Di Toro, Giulio

2017-04-01

Dolostone is the most dominant lithology of the seismogenic upper crust around the Mediterranean Sea. Understanding the internal mechanisms controlling fault friction is crucial for understanding seismicity along active faults. Displacement in such fault zones is frequently highlighted by highly reflective (mirror-like) slip surfaces, created by thin films of nanogranular fault rock. Using saw-cut dolostone samples coming from natural fault zones, we conducted friction experiments under triaxial loading conditions. To reproduce the natural conditions, experiments were conducted at 30, 60 and 90 MPa confining pressure at respectively 30, 65 and 100 degrees C. At 30 and 65 degrees C, only slow rupture was observed and the experimental fault exhibits frictional behaviour, i.e. a dependence of normal stress on peak shear stress. At 65 degrees C, a strengthening behaviour is observed after the main rupture, leading to a succession of slow rupture. At 100 degrees C, the macroscopic behaviour of the fault becomes ductile, and no dependence of pressure on the peak shear stress is observed. In addition, the increase of the confining pressure up to 60 and 90 MPa allow the transition from slow to fast rupture, highlighted by the records of acoustic activity and by dynamic stress drop occurring in a few tens of microseconds. Using strain gages located along the fault surface and acoustic transducers, we were able to measure the rupture velocities during slow and fast rupture. Slow ruptures propagated around 0.1 m/s, in agreement with natural observations. Fast ruptures propagated up the supershear velocities, i.e. faster than the shear wave speed (>3500 m/s). A complete study of the microstructures was realized before and after ruptures. Slow ruptures lead to the production of mirror-like surface driven by the production of nanograins due to dislocation processes. Fast ruptures induce the production of amorphous material along the fault surface, which may come from melting processes. We demonstrate that the transition from slow to dynamic instabilities is observed when the entire fault exhibits plastic processes, which increase the stiffness of the fault.

A Game Theoretic Fault Detection Filter

NASA Technical Reports Server (NTRS)

Chung, Walter H.; Speyer, Jason L.

1995-01-01

The fault detection process is modelled as a disturbance attenuation problem. The solution to this problem is found via differential game theory, leading to an H(sub infinity) filter which bounds the transmission of all exogenous signals save the fault to be detected. For a general class of linear systems which includes some time-varying systems, it is shown that this transmission bound can be taken to zero by simultaneously bringing the sensor noise weighting to zero. Thus, in the limit, a complete transmission block can he achieved, making the game filter into a fault detection filter. When we specialize this result to time-invariant system, it is found that the detection filter attained in the limit is identical to the well known Beard-Jones Fault Detection Filter. That is, all fault inputs other than the one to be detected (the "nuisance faults") are restricted to an invariant subspace which is unobservable to a projection on the output. For time-invariant systems, it is also shown that in the limit, the order of the state-space and the game filter can be reduced by factoring out the invariant subspace. The result is a lower dimensional filter which can observe only the fault to be detected. A reduced-order filter can also he generated for time-varying systems, though the computational overhead may be intensive. An example given at the end of the paper demonstrates the effectiveness of the filter as a tool for fault detection and identification.

Detection of gear cracks in a complex gearbox of wind turbines using supervised bounded component analysis of vibration signals collected from multi-channel sensors

NASA Astrophysics Data System (ADS)

Li, Zhixiong; Yan, Xinping; Wang, Xuping; Peng, Zhongxiao

2016-06-01

In the complex gear transmission systems, in wind turbines a crack is one of the most common failure modes and can be fatal to the wind turbine power systems. A single sensor may suffer with issues relating to its installation position and direction, resulting in the collection of weak dynamic responses of the cracked gear. A multi-channel sensor system is hence applied in the signal acquisition and the blind source separation (BSS) technologies are employed to optimally process the information collected from multiple sensors. However, literature review finds that most of the BSS based fault detectors did not address the dependence/correlation between different moving components in the gear systems; particularly, the popular used independent component analysis (ICA) assumes mutual independence of different vibration sources. The fault detection performance may be significantly influenced by the dependence/correlation between vibration sources. In order to address this issue, this paper presents a new method based on the supervised order tracking bounded component analysis (SOTBCA) for gear crack detection in wind turbines. The bounded component analysis (BCA) is a state of art technology for dependent source separation and is applied limitedly to communication signals. To make it applicable for vibration analysis, in this work, the order tracking has been appropriately incorporated into the BCA framework to eliminate the noise and disturbance signal components. Then an autoregressive (AR) model built with prior knowledge about the crack fault is employed to supervise the reconstruction of the crack vibration source signature. The SOTBCA only outputs one source signal that has the closest distance with the AR model. Owing to the dependence tolerance ability of the BCA framework, interfering vibration sources that are dependent/correlated with the crack vibration source could be recognized by the SOTBCA, and hence, only useful fault information could be preserved in the reconstructed signal. The crack failure thus could be precisely identified by the cyclic spectral correlation analysis. A series of numerical simulations and experimental tests have been conducted to illustrate the advantages of the proposed SOTBCA method for fatigue crack detection. Comparisons to three representative techniques, i.e. Erdogan's BCA (E-BCA), joint approximate diagonalization of eigen-matrices (JADE), and FastICA, have demonstrated the effectiveness of the SOTBCA. Hence the proposed approach is suitable for accurate gear crack detection in practical applications.

A fault tolerant 80960 engine controller

NASA Technical Reports Server (NTRS)

Reichmuth, D. M.; Gage, M. L.; Paterson, E. S.; Kramer, D. D.

1993-01-01

The paper describes the design of the 80960 Fault Tolerant Engine Controller for the supervision of engine operations, which was designed for the NASA Marshall Space Center. Consideration is given to the major electronic components of the controller, including the engine controller, effectors, and the sensors, as well as to the controller hardware, the controller module and the communications module, and the controller software. The architecture of the controller hardware allows modifications to be made to fit the requirements of any new propulsion systems. Multiple flow diagrams are presented illustrating the controller's operations.

A testing-coverage software reliability model considering fault removal efficiency and error generation.

PubMed

Li, Qiuying; Pham, Hoang

2017-01-01

In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance.

A fault-tolerant strategy based on SMC for current-controlled converters

NASA Astrophysics Data System (ADS)

Azer, Peter M.; Marei, Mostafa I.; Sattar, Ahmed A.

2018-05-01

The sliding mode control (SMC) is used to control variable structure systems such as power electronics converters. This paper presents a fault-tolerant strategy based on the SMC for current-controlled AC-DC converters. The proposed SMC is based on three sliding surfaces for the three legs of the AC-DC converter. Two sliding surfaces are assigned to control the phase currents since the input three-phase currents are balanced. Hence, the third sliding surface is considered as an extra degree of freedom which is utilised to control the neutral voltage. This action is utilised to enhance the performance of the converter during open-switch faults. The proposed fault-tolerant strategy is based on allocating the sliding surface of the faulty leg to control the neutral voltage. Consequently, the current waveform is improved. The behaviour of the current-controlled converter during different types of open-switch faults is analysed. Double switch faults include three cases: two upper switch fault; upper and lower switch fault at different legs; and two switches of the same leg. The dynamic performance of the proposed system is evaluated during healthy and open-switch fault operations. Simulation results exhibit the various merits of the proposed SMC-based fault-tolerant strategy.

Surface faulting along the inland Itozawa normal fault (eastern Japan) and relation to the 2011 Tohoku-oki megathrust earthquake

NASA Astrophysics Data System (ADS)

Ferry, M.; Tsutsumi, H.; Meghraoui, M.; Toda, S.

2012-12-01

The 11 March 2011 Mw 9 Tohoku-oki earthquake ruptured ~500 km length of the Japan Trench along the coast of eastern Japan and significantly impacted the stress regime within the crust. The resulting change in seismicity over the Japan mainland was exhibited by the 11 April 2011 Mw 6.6 Iwaki earthquake that ruptured the Itozawa and Yunodake faults. Trending NNW and NW, respectively, these 70-80° W-dipping faults bound the Iwaki basin of Neogene age and have been reactivated simultaneously both along 15-km-long sections. Here, we present initial results from a paleoseismic excavation performed across the Itozawa fault within the Tsunagi Valley at the northern third of the observed surface rupture. At the Tsunagi site, the rupture affects a rice paddy, which provides an ideally horizontal initial state to collect detailed and accurate measurements. The surface break is composed of a continuous 30-to-40-cm-wide purely extensional crack that separates the uplifted block from a gently dipping 1-to-2-m-wide strip affected by right-stepping en-echelon cracks and locally bounded by a ~0.1-m-high reverse scarplet. Total station across-fault topographic profiles indicate the pre-earthquake ground surface was vertically deformed by ~0.6 m while direct field examinations reveal that well-defined rice paddy limits have been left-laterally offset by ~0.1 m. The 12-m-long, 3.5-m-deep trench exposes the 30-to-40-cm-thick cultivated soil overlaying a 1-m-thick red to yellow silt unit, a 2-m-thick alluvial gravel unit and a basal 0.1-1-m-thick organic-rich silt unit. Deformation associated to the 2011 rupture illustrates down-dip movement along a near-vertical fault with a well-expressed bending moment at the surface and generalized warping. On the north wall, the intermediate gravel unit displays a deformation pattern similar to granular flow with only minor discrete faulting and no splay to be continuously followed from the main fault to the surface. On the south wall, warping dominates as well but with some strain localization along two major splays that exhibit 15-20 cm of vertical offset. On both walls, the basal silt unit is vertically deformed by ~0.6 m, similarly to what is observed for the 2011 rupture. Furthermore, the base of said silt unit exhibits indication for secondary faulting prior to the 2011 event and that resemble cracks observed at the present-day surface. This suggests that the Itozawa fault has already ruptured in a similar fashion; probably in the late Pleistocene-early Holocene (radiocarbon samples are being processed). Hence, recent activity of the Itozawa fault may be controlled entirely by large to giant earthquakes along the Japan Trench.

Interconnection requirements in avionic systems

NASA Astrophysics Data System (ADS)

Vergnolle, Claude; Houssay, Bruno

1991-04-01

The future aircraft generation will have thousand smart electromagnetic sensors distributed allover. Each sensor is connected with fibers links to the main-frame computer in charge of the real time signal''s correlation. Such a computer must be compactly built and massively parallel: it needs the use of 3 D optical free-space interconnect between neighbouring boards and reconfigurable interconnects via holographic backplane. The optical interconnect facilities will be also used to build fault-tolerant computer through large redundancy.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpson, L.; Britt, J.; Birkmire, R.

ITN Energy Systems, Inc., and Global Solar Energy, Inc., assisted by NREL's PV Manufacturing R&D program, have continued to advance CIGS production technology by developing trajectory-oriented predictive/control models, fault-tolerance control, control platform development, in-situ sensors, and process improvements. Modeling activities included developing physics-based and empirical models for CIGS and sputter-deposition processing, implementing model-based control, and applying predictive models to the construction of new evaporation sources and for control. Model-based control is enabled by implementing reduced or empirical models into a control platform. Reliability improvement activities include implementing preventive maintenance schedules; detecting failed sensors/equipment and reconfiguring to tinue processing; and systematicmore » development of fault prevention and reconfiguration strategies for the full range of CIGS PV production deposition processes. In-situ sensor development activities have resulted in improved control and indicated the potential for enhanced process status monitoring and control of the deposition processes. Substantial process improvements have been made, including significant improvement in CIGS uniformity, thickness control, efficiency, yield, and throughput. In large measure, these gains have been driven by process optimization, which in turn have been enabled by control and reliability improvements due to this PV Manufacturing R&D program.« less

Detecting Solenoid Valve Deterioration in In-Use Electronic Diesel Fuel Injection Control Systems

PubMed Central

Tsai, Hsun-Heng; Tseng, Chyuan-Yow

2010-01-01

The diesel engine is the main power source for most agricultural vehicles. The control of diesel engine emissions is an important global issue. Fuel injection control systems directly affect fuel efficiency and emissions of diesel engines. Deterioration faults, such as rack deformation, solenoid valve failure, and rack-travel sensor malfunction, are possibly in the fuel injection module of electronic diesel control (EDC) systems. Among these faults, solenoid valve failure is most likely to occur for in-use diesel engines. According to the previous studies, this failure is a result of the wear of the plunger and sleeve, based on a long period of usage, lubricant degradation, or engine overheating. Due to the difficulty in identifying solenoid valve deterioration, this study focuses on developing a sensor identification algorithm that can clearly classify the usability of the solenoid valve, without disassembling the fuel pump of an EDC system for in-use agricultural vehicles. A diagnostic algorithm is proposed, including a feedback controller, a parameter identifier, a linear variable differential transformer (LVDT) sensor, and a neural network classifier. Experimental results show that the proposed algorithm can accurately identify the usability of solenoid valves. PMID:22163597

Detecting solenoid valve deterioration in in-use electronic diesel fuel injection control systems.

PubMed

Tsai, Hsun-Heng; Tseng, Chyuan-Yow

2010-01-01

The diesel engine is the main power source for most agricultural vehicles. The control of diesel engine emissions is an important global issue. Fuel injection control systems directly affect fuel efficiency and emissions of diesel engines. Deterioration faults, such as rack deformation, solenoid valve failure, and rack-travel sensor malfunction, are possibly in the fuel injection module of electronic diesel control (EDC) systems. Among these faults, solenoid valve failure is most likely to occur for in-use diesel engines. According to the previous studies, this failure is a result of the wear of the plunger and sleeve, based on a long period of usage, lubricant degradation, or engine overheating. Due to the difficulty in identifying solenoid valve deterioration, this study focuses on developing a sensor identification algorithm that can clearly classify the usability of the solenoid valve, without disassembling the fuel pump of an EDC system for in-use agricultural vehicles. A diagnostic algorithm is proposed, including a feedback controller, a parameter identifier, a linear variable differential transformer (LVDT) sensor, and a neural network classifier. Experimental results show that the proposed algorithm can accurately identify the usability of solenoid valves.

Investigation of hydrologic and biogeochemical controls on arsenic mobilization using distributed sensing at a field site in Munshiganj, Bangladesh

NASA Astrophysics Data System (ADS)

Ramanathan, N.; Estrin, D.; Harmon, T.; Harvey, C.; Jay, J.; Kohler, E.; Rothenberg, S.

2006-12-01

The presence of arsenic in the groundwater has led to the largest environmental poisoning in history; tens of millions of people in the Ganges Delta continue to drink groundwater that is dangerously contaminated with arsenic. A current working hypothesis is that arsenic is mobilized in the near surface environment where sediments are weathered by seasonal changes in the redox state that drive a cycle of pyrite oxidation and iron oxide reduction. In order to test the supporting hypothesis that subsurface geochemical changes may be induced by agricultural activity, we deployed 42 wirelessly networked ion-selective electrodes, including calcium, ammonium, nitrate, ORP, chloride, carbonate, and pH in a rice paddy in the Munshiganj district of Bangladesh in January of 2006. Each sensor was connected to an MDA300 sensor board and Mica2 wireless transceiver and computational device. Over a period of 11 days, we observed clear diel, and diurnal trends in 4 of the electrodes (calcium, ammonium, chloride and carbonate). The trends may be due to hydrological changes, or geochemical changes induced either by photosynthesis in the overlying water (which then infiltrated to the depth of the sensors) or in the root zone of rice plants. While the spatiotemporally dense measurements from wireless sensor networks enable scientists to ask new questions and elucidate complex relationships in heterogeneous physical environments such as soil, there are many practical issues to address in order to collect data usable for scientific purposes. For example, in response to a stream of faults in one of our sensor network deployments, we designed Sympathy to enable users to find and fix problems impacting the quantity of data collected in the field. Sympathy detects packet loss experienced at the base station and systematically assigns blame to faulty components in the network for remediation, replacing the prior policy of ad-hoc node rebooting and battery replacements. Sympathy has been deployed in many habitat monitoring sensor networks. While using Sympathy at our Bangladesh field site we received 80% of the sensor data expected at the base station, upon returning, post-deployment analysis revealed that 42% of these sensor data were potentially faulty. Due to the remote location of the deployment, we were unable to go back and validate the questionable segments of the data set, forcing us to discard potentially interesting information. In addition to being undesirable, this response is often avoidable as well. Even simple actions such as checking sensor connections and quickly validating sensors in the field could have increased our confidence in the quality of the data, minimizing doubts that data observations were simply caused by badly behaving hardware. To improve data quality, we have designed a system called Confidence, which continuously monitors data collected at a base-station to identify faulty data and notify the user in the field of actions they can take to validate the data or remediate the sensor fault. Augmenting a sensor network deployment with Confidence and Sympathy enables users in the identification and remediation of faults impacting the quality and quantity of data respectively.

Integrated Multidisciplinary Fault Observation System in the western part of the main Marmara Fault in the frame of an EU-FP7 project, titled as MARSITE

NASA Astrophysics Data System (ADS)

Ozel, Oguz; Guralp, Cansun; Tunc, Suleyman; Yalcinkaya, Esref; Meral Ozel, Nurcan

2015-04-01

The main objective of this study is to install a multi-parameter borehole system and surface array consisting of eight broadband sensors as close to the main Marmara Fault (MMF) in the western Marmara Sea as possible, and measure continuously the evolution of the state of the fault zone surrounding the MMF and to detect any anomaly or change which may occur before earthquakes by making use of the data from these arrays. The multi-parameter borehole system is composed of very wide dynamic range and stable borehole (VBB) broad band seismic sensor, and incorporate 3-D strain meter, tilt meter, and temperature and local hydrostatic pressure measuring devices. All these sensors are installed in 146m-deep borehole. All the sensor outputs are digitized; total of 11*24 bit-channels and 6*20 bit-channels. Real-time data transmission to the main server of the Marsite Project at Kandilli Observatory in Istanbul is accomplished. The multi-parameter borehole seismic station uses the latest update technologies and design ideas to record "Earth tides" signals to the smallest magnitude -3 events, as the innovative part of the Marsite Project. Bringing face to face the seismograms of microearthquakes recorded by borehole and surface instruments portrays quite different contents. The shorter recording duration and nearly flat frequency spectrum up to the Nyquist frequencies of borehole records are faced with longer recording duration and rapid decay of spectral amplitudes at higher frequencies of a surface seismogram. The main causative of the observed differences are near surface geology effects that mask most of the source related information the seismograms include, and that give rise to scattering, generating longer duration seismograms. In view of these circumstances, studies on microearthquakes employing surface seismograms may bring on misleading results. Particularly, the works on earthquake physics and nucleation process of earthquakes requires elaborate analysis of tiny events. It is obvious from the studies on the nucleation process of the 1999 earthquake that tens of minutes before the major rupture initiate noteworthy microearthquake activity happened. The starting point of the 1999 rupture was a site of swarm activity noticed a few decades prior the main shock. Nowadays, analogous case is probable in western Marmara Sea region, prone to a major event in near future where the seismic activity is prevailing along the impending rupture zone. Having deployed a borehole system at the eastern end of the Ganos fault zone will yield invaluable data to closely inspect and monitor the last stages of the preparation stage of major rupture.

Multi-hop routing mechanism for reliable sensor computing.

PubMed

Chen, Jiann-Liang; Ma, Yi-Wei; Lai, Chia-Ping; Hu, Chia-Cheng; Huang, Yueh-Min

2009-01-01

Current research on routing in wireless sensor computing concentrates on increasing the service lifetime, enabling scalability for large number of sensors and supporting fault tolerance for battery exhaustion and broken nodes. A sensor node is naturally exposed to various sources of unreliable communication channels and node failures. Sensor nodes have many failure modes, and each failure degrades the network performance. This work develops a novel mechanism, called Reliable Routing Mechanism (RRM), based on a hybrid cluster-based routing protocol to specify the best reliable routing path for sensor computing. Table-driven intra-cluster routing and on-demand inter-cluster routing are combined by changing the relationship between clusters for sensor computing. Applying a reliable routing mechanism in sensor computing can improve routing reliability, maintain low packet loss, minimize management overhead and save energy consumption. Simulation results indicate that the reliability of the proposed RRM mechanism is around 25% higher than that of the Dynamic Source Routing (DSR) and ad hoc On-demand Distance Vector routing (AODV) mechanisms.

Stacking fault density and bond orientational order of fcc ruthenium nanoparticles

NASA Astrophysics Data System (ADS)

Seo, Okkyun; Sakata, Osami; Kim, Jae Myung; Hiroi, Satoshi; Song, Chulho; Kumara, Loku Singgappulige Rosantha; Ohara, Koji; Dekura, Shun; Kusada, Kohei; Kobayashi, Hirokazu; Kitagawa, Hiroshi

2017-12-01

We investigated crystal structure deviations of catalytic nanoparticles (NPs) using synchrotron powder X-ray diffraction. The samples were fcc ruthenium (Ru) NPs with diameters of 2.4, 3.5, 3.9, and 5.4 nm. We analyzed average crystal structures by applying the line profile method to a stacking fault model and local crystal structures using bond orientational order (BOO) parameters. The reflection peaks shifted depending on rules that apply to each stacking fault. We evaluated the quantitative stacking faults densities for fcc Ru NPs, and the stacking fault per number of layers was 2-4, which is quite large. Our analysis shows that the fcc Ru 2.4 nm-diameter NPs have a considerably high stacking fault density. The B factor tends to increase with the increasing stacking fault density. A structural parameter that we define from the BOO parameters exhibits a significant difference from the ideal value of the fcc structure. This indicates that the fcc Ru NPs are highly disordered.

Spatial variability of damage around faults in the Joe Lott Tuff Member of the Mount Belknap Volcanics, southwestern Utah: An analog to faulting in tuff on Mars

NASA Astrophysics Data System (ADS)

Okubo, C. H.

2011-12-01

The equatorial layered deposits on Mars exhibit abundant evidence for the sustained presence of groundwater, and therefore insight into past water-related processes may be gained through the study of these deposits. Pyroclastic and evaporitic sediments are two broad lithologies that are known or inferred to comprise these deposits. Investigations into the effects of faulting on fluid flow potential through such Mars analog lithologies have been limited. Thus a study into the effects of faulting on fluid flow pathways through fine-grained pyroclastic sediments has been undertaken, and the results of this study are presented here. Faults and their damage zones can influence the trapping and migration of fluids by acting as either conduits or barriers to fluid flow. In clastic sedimentary rocks, the conductivity of fault damage zones is primarily a function of the microstructure of the host rock, stress history, phyllosilicate content, and cementation. The chemical composition of the host rock influences the mechanical strength of the grains, the susceptibility of the grains to alteration, and the availability of authigenic cements. The spatial distribution of fault-related damage is investigated within the Joe Lott Tuff Member of the Mount Belknap Volcanics, Utah. Damage is characterized by measuring fracture densities along the fault, and by mapping the gas permeability of the surrounding rock. The Joe Lott Tuff is a partially welded, crystal-poor, rhyolite ash-flow tuff of Miocene age. While the rhyolitic chemical composition of the Joe Lott Tuff is not analogous to the basaltic compositions expected for Mars, the mechanical behavior of a poorly indurated mixture of fine-grained glass and pumice is pertinent to understanding the fundamental mechanics of faulting in Martian pyroclastic sediments. Results of mapping around two faults are presented here. The first fault is entirely exposed in cross-section and has a down-dip height of ~10 m. The second fault is partially exposed, with ~21 m visible in cross-section. Both faults have a predominantly normal sense of offset and a minor dextral strike-slip component. The 10 m fault has a single well-defined surface, while the 21 m fault takes the form of a 5-10 cm wide fault core. Fracture density at the 10 m fault is highest near its upper and lower tips, forming distinct near-tip fracture damage zones. At the 21 m fault, fracture density is broadly consistent along the exposed height of the fault, with the highest fracture densities nearest to the fault core. Fracture density is higher in the hanging walls than in the footwalls of both faults, and the footwall of the 21 m fault exhibits m-scale areas of significant distributed cataclasis. Gas permeability has a marked decrease, several orders of magnitude relative to the non-deformed host rock, at 1.5 m on either side of the 10 m fault. Permeability is lowest outboard of the fault's near-tip fracture damage zones. A similar permeability drop occurs at 1-5 m from the center of the 21 m fault's core, with the permeability drop extending furthest from the fault core in the footwall. These findings will be used to improve existing numerical methods for predicting subsurface fluid flow patterns from observed fault geometries on Mars.

Emergent Adaptive Noise Reduction from Communal Cooperation of Sensor Grid

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Jones, Michael G.; Nark, Douglas M.; Lodding, Kenneth N.

2010-01-01

In the last decade, the realization of small, inexpensive, and powerful devices with sensors, computers, and wireless communication has promised the development of massive sized sensor networks with dense deployments over large areas capable of high fidelity situational assessments. However, most management models have been based on centralized control and research has concentrated on methods for passing data from sensor devices to the central controller. Most implementations have been small but, as it is not scalable, this methodology is insufficient for massive deployments. Here, a specific application of a large sensor network for adaptive noise reduction demonstrates a new paradigm where communities of sensor/computer devices assess local conditions and make local decisions from which emerges a global behaviour. This approach obviates many of the problems of centralized control as it is not prone to single point of failure and is more scalable, efficient, robust, and fault tolerant

Integrating physically based simulators with Event Detection Systems: Multi-site detection approach.

PubMed

Housh, Mashor; Ohar, Ziv

2017-03-01

The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network. In this study, we show that a model-based EDS which utilizes a physically based water quality and hydraulics simulation models, can outperform the signal processing based EDS. We also show that the model-based EDS can facilitate the development of a Multi-Site EDS (MSEDS), which analyzes the data from all the monitoring stations simultaneously within an integrated network. The advantage of the joint analysis in the MSEDS is expressed by increased detection accuracy (higher true positive alarms and fewer false alarms) and shorter detection time. Copyright © 2016 Elsevier Ltd. All rights reserved.

A formally verified algorithm for interactive consistency under a hybrid fault model

NASA Technical Reports Server (NTRS)

Lincoln, Patrick; Rushby, John

1993-01-01

Consistent distribution of single-source data to replicated computing channels is a fundamental problem in fault-tolerant system design. The 'Oral Messages' (OM) algorithm solves this problem of Interactive Consistency (Byzantine Agreement) assuming that all faults are worst-cass. Thambidurai and Park introduced a 'hybrid' fault model that distinguished three fault modes: asymmetric (Byzantine), symmetric, and benign; they also exhibited, along with an informal 'proof of correctness', a modified version of OM. Unfortunately, their algorithm is flawed. The discipline of mechanically checked formal verification eventually enabled us to develop a correct algorithm for Interactive Consistency under the hybrid fault model. This algorithm withstands $a$ asymmetric, $s$ symmetric, and $b$ benign faults simultaneously, using $m+1$ rounds, provided $n is greater than 2a + 2s + b + m$, and $m\\geg a$. We present this algorithm, discuss its subtle points, and describe its formal specification and verification in PVS. We argue that formal verification systems such as PVS are now sufficiently effective that their application to fault-tolerance algorithms should be considered routine.

The 7.9 Denali Fault Earthquake: Aftershock Locations, Moment Tensors and Focal Mechanisms from the Regional Seismic Network Data

NASA Astrophysics Data System (ADS)

Ratchkovski, N. A.; Hansen, R. A.; Christensen, D.; Kore, K.

2002-12-01

The largest earthquake ever recorded on the Denali fault system (magnitude 7.9) struck central Alaska on November 3, 2002. It was preceded by a magnitude 6.7 foreshock on October 23. This earlier earthquake and its zone of aftershocks were located slightly to the west of the 7.9 quake. Aftershock locations and surface slip observations from the 7.9 quake indicate that the rupture was predominately unilateral in the eastward direction. Near Mentasta Lake, a village that experienced some of the worst damage in the quake, the surface rupture scar turns from the Denali fault to the adjacent Totschunda fault, which trends toward more southeasterly toward the Canadian border. Overall, the geologists found that measurable scarps indicate that the north side of the Denali fault moved to the east and vertically up relative to the south. Maximum offsets on the Denali fault were 8.8 meters at the Tok Highway cutoff, and were 2.2 meters on the Totschunda fault. The Alaska regional seismic network consists of over 250 station sites, operated by the Alaska Earthquake Information Center (AEIC), the Alaska Volcano Observatory (AVO), and the Pacific Tsunami Warning Center (PTWC). Over 25 sites are equipped with the broad-band sensors, some of which have in addition the strong motion sensors. The rest of the stations are either 1 or 3-component short-period instruments. The data from these stations are collected, processed and archived at the AEIC. The AEIC staff installed a temporary network with over 20 instruments following the 6.7 Nenana Mountain and the 7.9 events. Prior to the M 7.9 Denali Fault event, the automatic earthquake detection system at AEIC was locating between 15 and 30 events per day. After the event, the system had over 200-400 automatic locations per day for at least 10 days following the 7.9 event. The processing of the data is ongoing with the priority given to the larger events. The cumulative length of the 6.7 and 7.9 aftershock locations along the Denali and Totschunda faults is about 300 km. We will present the aftershock locations, first motion focal mechanisms for M4+ events and regional moment tensors for M4.5+ events. The first motion focal mechanism for the main event indicates thrusting on the NE-trending plane with a dip of 48 degrees. We will present results of the double difference relocation of the aftershocks of the M7.9 event. The relocated aftershocks indicate a NW-dipping fault plane in the epicentral area of the event and a vertical plane along the rest of the rupture length.

Heterogeneous State of Stress and Seismicity Distribution Along the San Andreas Fault in Southern California: New Insights into Rupture Terminations of Past Earthquakes

NASA Astrophysics Data System (ADS)

Hauksson, E.; Ross, Z. E.; Yu, C.

2016-12-01

The southern San Andreas Fault (SAF) accommodates 80% of the plate motion between the Pacific and North America plates in southern California. We image complex patterns of the state of stress, style of faulting, and seismicity adjacent to the SAF, both along strike and away from the fault. This complexity is not captured in previous one-dimensional profiles of stress orientations across the fault. On average the maximum principal stress (S1) rotates from N30°E in central California, along the Cholame segment, to N0°-20°W along the Mojave and San Bernardino segments. Farther south, along the Coachella Valley segment the orientation is again N30°E. On a broad scale these changes in S1 orientation coincide with the more westerly strike of the SAF across the Mojave Desert but in detail they suggest significant variations in frictional coefficient or strength along strike. Similarly, on a more detailed scale, the size of the S1 rotations is spatially heterogeneous, with the largest rotations associated with the two bends in the SAF, at Gorman and Cajon Pass. In each location a major fault, Garlock fault and San Jacinto fault, intersects the SAF. In these intersected regions, the seismicity is more abundant and the S1 orientation is more likely to exhibit abrupt changes in trend by up to 10° across the fault. The GPS maximum principal strain rate orientations exhibit a similar but smoother pattern with mostly west of north orientations along the Mojave and San Bernardino segments. The style of faulting as derived from stress inversion is similarly heterogeneous with a mixture of strike-slip and thrust faulting forming complex spatial patterns. The D95% maximum depth of earthquakes changes abruptly both along and across the SAF suggesting that local variations in composition affect the maximum seismicity depth. The heterogeneity in the state of stress is not influenced by the average heat flow, which is similar along the whole length of the southern SAF. The crustal composition, background seismicity, and the strength of the SAF vary along strike, with the strongest fault segments being near the two bends, Gorman and Cajon Pass, where past major earthquake ruptures may have preferentially terminated.

Initial results on fault diagnosis of DSN antenna control assemblies using pattern recognition techniques

NASA Technical Reports Server (NTRS)

Smyth, P.; Mellstrom, J.

1990-01-01

Initial results obtained from an investigation using pattern recognition techniques for identifying fault modes in the Deep Space Network (DSN) 70 m antenna control loops are described. The overall background to the problem is described, the motivation and potential benefits of this approach are outlined. In particular, an experiment is described in which fault modes were introduced into a state-space simulation of the antenna control loops. By training a multilayer feed-forward neural network on the simulated sensor output, classification rates of over 95 percent were achieved with a false alarm rate of zero on unseen tests data. It concludes that although the neural classifier has certain practical limitations at present, it also has considerable potential for problems of this nature.

Fault Accommodation in Control of Flexible Systems

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Sparks, Dean W., Jr.; Lim, Kyong B.

1998-01-01

New synthesis techniques for the design of fault accommodating controllers for flexible systems are developed. Three robust control design strategies, static dissipative, dynamic dissipative and mu-synthesis, are used in the approach. The approach provides techniques for designing controllers that maximize, in some sense, the tolerance of the closed-loop system against faults in actuators and sensors, while guaranteeing performance robustness at a specified performance level, measured in terms of the proximity of the closed-loop poles to the imaginary axis (the degree of stability). For dissipative control designs, nonlinear programming is employed to synthesize the controllers, whereas in mu-synthesis, the traditional D-K iteration is used. To demonstrate the feasibility of the proposed techniques, they are applied to the control design of a structural model of a flexible laboratory test structure.

Practical comparison of distributed ledger technologies for IoT

NASA Astrophysics Data System (ADS)

Red, Val A.

2017-05-01

Existing distributed ledger implementations - specifically, several blockchain implementations - embody a cacophony of divergent capabilities augmenting innovations of cryptographic hashes, consensus mechanisms, and asymmetric cryptography in a wide variety of applications. Whether specifically designed for cryptocurrency or otherwise, several distributed ledgers rely upon modular mechanisms such as consensus or smart contracts. These components, however, can vary substantially among implementations; differences involving proof-of-work, practical byzantine fault tolerance, and other consensus approaches exemplify distinct distributed ledger variations. Such divergence results in unique combinations of modules, performance, latency, and fault tolerance. As implementations continue to develop rapidly due to the emerging nature of blockchain technologies, this paper encapsulates a snapshot of sensor and internet of things (IoT) specific implementations of blockchain as of the end of 2016. Several technical risks and divergent approaches preclude standardization of a blockchain for sensors and IoT in the foreseeable future; such issues will be assessed alongside the practicality of IoT applications among Hyperledger, Iota, and Ethereum distributed ledger implementations suggested for IoT. This paper contributes a comparison of existing distributed ledger implementations intended for practical sensor and IoT utilization. A baseline for characterizing distributed ledger implementations in the context of IoT and sensors is proposed. Technical approaches and performance are compared considering IoT size, weight, and power limitations. Consensus and smart contracts, if applied, are also analyzed for the respective implementations' practicality and security. Overall, the maturity of distributed ledgers with respect to sensor and IoT applicability will be analyzed for enterprise interoperability.

Theoretical study of surface plasmon resonance sensors based on 2D bimetallic alloy grating

NASA Astrophysics Data System (ADS)

Dhibi, Abdelhak; Khemiri, Mehdi; Oumezzine, Mohamed

2016-11-01

A surface plasmon resonance (SPR) sensor based on 2D alloy grating with a high performance is proposed. The grating consists of homogeneous alloys of formula MxAg1-x, where M is gold, copper, platinum and palladium. Compared to the SPR sensors based a pure metal, the sensor based on angular interrogation with silver exhibits a sharper (i.e. larger depth-to-width ratio) reflectivity dip, which provides a big detection accuracy, whereas the sensor based on gold exhibits the broadest dips and the highest sensitivity. The detection accuracy of SPR sensor based a metal alloy is enhanced by the increase of silver composition. In addition, the composition of silver which is around 0.8 improves the sensitivity and the quality of SPR sensor of pure metal. Numerical simulations based on rigorous coupled wave analysis (RCWA) show that the sensor based on a metal alloy not only has a high sensitivity and a high detection accuracy, but also exhibits a good linearity and a good quality.

Seismic Velocity Variation and Evolution of the Upper Oceanic Crust across the Mid-Atlantic Ridge at 1.3°S

NASA Astrophysics Data System (ADS)

Jian, H.; Singh, S. C.

2017-12-01

The oceanic crust that covers >70% of the solid earth is formed at mid-ocean ridges, but get modified as it ages. Understanding the evolution of oceanic crust requires investigations of crustal structures that extend from zero-age on the ridge axis to old crust. In this study, we analyze a part of a 2000-km-long seismic transect that crosses the Mid-Atlantic Ridge segment at 1.3°S, south of the Chain transform fault. The seismic data were acquired using a 12-km-long multi-sensor streamer and dense air-gun shots. Using a combination of downward continuation and seismic tomography methods, we have derived a high-resolution upper crustal velocity structure down to 2-2.5 km depth below the seafloor, from the ridge axis to 3.5 Ma on both sides of the ridge axis. The results demonstrate that velocities increase at all depths in the upper crust as the crust ages, suggesting that hydrothermal precipitations seal the upper crustal pore spaces. This effect is most significant in layer 2A, causing a velocity increase of 0.5-1 km/s after 1-1.5 Ma, beyond which the velocity increase is very small. Furthermore, the results exhibit a significant decrease in both the frequency and amplitude of the low-velocity anomalies associated with faults beyond 1-1.5 Ma, when faults become inactive, suggesting a linkage between the sealing of fault space and the extinction of hydrothermal activity. Besides, the off-axis velocities are systematically higher on the eastern side of the ridge axis compared to on the western side, suggesting that a higher hydrothermal activity should exist on the outside-corner ridge flank than on the inside-corner flank. While the tomography results shown here cover 0-3.5 Ma crust, the ongoing research will further extend the study area to older crust and also incorporating pre-stack migration and full waveform inversion methods to improve the seismic structure.

Condition Monitoring of Helicopter Gearboxes by Embedded Sensing

NASA Technical Reports Server (NTRS)

Suryavanashi, Abhijit; Wang, Shengda; Gao, Robert; Danai, Kourosh; Lewicki, David G.

2002-01-01

Health of helicopter gearboxes is commonly assessed by monitoring the housing vibration, thus it is challenged by poor signal-to-noise ratio of the signal measured away from the source. It is hypothesized that vibration measurements from sensors placed inside the gearbox will be much clearer indicators of faults and will eliminate many of the difficulties faced by present condition monitoring systems. This paper outlines our approach to devising such a monitoring system. Several tasks have been outlined toward this objective and the strategy to address each has been described. Among the tasks are wireless sensor design, antenna design, and selection of sensor locations.

A testing-coverage software reliability model considering fault removal efficiency and error generation

PubMed Central

Li, Qiuying; Pham, Hoang

2017-01-01

In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance. PMID:28750091

Sensor for detecting changes in magnetic fields

DOEpatents

Praeg, Walter F.

1981-01-01

A sensor for detecting changes in the magnetic field of the equilibrium-field coil of a Tokamak plasma device comprises a pair of bifilar wires disposed circumferentially, one inside and one outside the equilibrium-field coil. Each is shorted at one end. The difference between the voltages detected at the other ends of the bifilar wires provides a measure of changing flux in the equilibrium-field coil. This difference can be used to detect faults in the coil in time to take action to protect the coil.

Report on Contract F05611-76-90203. Volume I.

DTIC Science & Technology

1976-01-01

The most serious of these assumptions used in the development of the algorithms was that the sensors in use are much better than they really are...This was not the fault of the AFIT personnel, since the algo- rithus they developed used the best information about the sensors which they had at the...phase response for the first FIR filters. In the region trom 0.01 Hz to 0.1 liz the tiltmeter controls the servo. However, above 0. Ili the seismo-neter

The patient-sensor interface

PubMed Central

Crockett, G. S.

1970-01-01

During the assessment of monitoring equipment on acute medical cases in a general ward, a quantitative investigation of technical faults revealed that 44% of these occurred at the patient-sensor interface. While the attachment of the equipment was accepted by the patient and was suitable for application by nursing staff, this degree of technical breakdown indicates that more progress is necessary in the design of this aspect of monitoring equipment before it is possible to have a reliable system. ImagesFig. 1 PMID:5476136

Analytical redundancy management mechanization and flight data analysis for the F-8 digital fly-by-wire aircraft flight control sensors

NASA Technical Reports Server (NTRS)

Deckert, J. C.

1983-01-01

The details are presented of an onboard digital computer algorithm designed to reliably detect and isolate the first failure in a duplex set of flight control sensors aboard the NASA F-8 digital fly-by-wire aircraft. The algorithm's successful flight test program is summarized, and specific examples are presented of algorithm behavior in response to software-induced signal faults, both with and without aircraft parameter modeling errors.

An Efficient Reachability Analysis Algorithm

NASA Technical Reports Server (NTRS)

Vatan, Farrokh; Fijany, Amir

2008-01-01

A document discusses a new algorithm for generating higher-order dependencies for diagnostic and sensor placement analysis when a system is described with a causal modeling framework. This innovation will be used in diagnostic and sensor optimization and analysis tools. Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in-situ platforms. This algorithm will serve as a power tool for technologies that satisfy a key requirement of autonomous spacecraft, including science instruments and in-situ missions.

The Quaternary Silver Creek Fault Beneath the Santa Clara Valley, California

USGS Publications Warehouse

Wentworth, Carl M.; Williams, Robert A.; Jachens, Robert C.; Graymer, Russell W.; Stephenson, William J.

2010-01-01

The northwest-trending Silver Creek Fault is a 40-km-long strike-slip fault in the eastern Santa Clara Valley, California, that has exhibited different behaviors within a changing San Andreas Fault system over the past 10-15 Ma. Quaternary alluvium several hundred meters thick that buries the northern half of the Silver Creek Fault, and that has been sampled by drilling and imaged in a detailed seismic reflection profile, provides a record of the Quaternary history of the fault. We assemble evidence from areal geology, stratigraphy, paleomagnetics, ground-water hydrology, potential-field geophysics, and reflection and earthquake seismology to determine the long history of the fault in order to evaluate its current behavior. The fault formed in the Miocene more than 100 km to the southeast, as the southwestern fault in a 5-km-wide right step to the Hayward Fault, within which the 40-km-long Evergreen pull-apart basin formed. Later, this basin was obliquely cut by the newly recognized Mt. Misery Fault to form a more direct connection to the Hayward Fault, although continued growth of the basin was sufficient to accommodate at least some late Pliocene alluvium. Large offset along the San Andreas-Calaveras-Mt Misery-Hayward Faults carried the basin northwestward almost to its present position when, about 2 Ma, the fault system was reorganized. This led to near abandonment of the faults bounding the pull-apart basin in favor of right slip extending the Calaveras Fault farther north before stepping west to the Hayward Fault, as it does today. Despite these changes, the Silver Creek Fault experienced a further 200 m of dip slip in the early Quaternary, from which we infer an associated 1.6 km or so of right slip, based on the ratio of the 40-km length of the strike-slip fault to a 5-km depth of the Evergreen Basin. This dip slip ends at a mid-Quaternary unconformity, above which the upper 300 m of alluvial cover exhibits a structural sag at the fault that we interpret as a negative flower structure. This structure implies some continuing strike slip on the Silver Creek Fault in the late Quaternary as well, with a transtensional component but no dip slip. Our only basis for estimating the rate of this later Quaternary strike slip on the Silver Creek Fault is to assume continuation of the inferred early Quaternary rate of less than 2 mm/yr. Faulting evident in a detailed seismic reflection profile across the Silver Creek Fault extends up to the limit of data at a depth of 50 m and age of about 140 ka, and the course of Coyote Creek suggests Holocene capture in a structural depression along the fault. No surface trace is evident on the alluvial plain, however, and convincing evidence of Holocene offset is lacking. Few instrumentally recorded earthquakes are located near the fault, and those that are near its southern end represent cross-fault shortening, not strike slip. The fault might have been responsible, however, for two poorly located moderate earthquakes that occurred in the area in 1903. Its southeastern end does mark an abrupt change in the pattern of abundant instrumentally recorded earthquakes along the Calaveras Fault-in both its strike and in the depth distribution of hypocenters-that could indicate continuing influence by the Silver Creek Fault. In the absence of convincing evidence to the contrary, and as a conservative estimate, we presume that the Silver Creek Fault has continued its strike-slip movement through the Holocene, but at a very slow rate. Such a slow rate would, at most, yield very infrequent damaging earthquakes. If the 1903 earthquakes did, in fact, occur on the Silver Creek Fault, they would have greatly reduced the short-term future potential for large earthquakes on the fault.

Foreshocks during the nucleation of stick-slip instability

USGS Publications Warehouse

McLaskey, Gregory C.; Kilgore, Brian D.

2013-01-01

We report on laboratory experiments which investigate interactions between aseismic slip, stress changes, and seismicity on a critically stressed fault during the nucleation of stick-slip instability. We monitor quasi-static and dynamic changes in local shear stress and fault slip with arrays of gages deployed along a simulated strike-slip fault (2 m long and 0.4 m deep) in a saw cut sample of Sierra White granite. With 14 piezoelectric sensors, we simultaneously monitor seismic signals produced during the nucleation phase and subsequent dynamic rupture. We observe localized aseismic fault slip in an approximately meter-sized zone in the center of the fault, while the ends of the fault remain locked. Clusters of high-frequency foreshocks (Mw ~ −6.5 to −5.0) can occur in this slowly slipping zone 5–50 ms prior to the initiation of dynamic rupture; their occurrence appears to be dependent on the rate at which local shear stress is applied to the fault. The meter-sized nucleation zone is generally consistent with theoretical estimates, but source radii of the foreshocks (2 to 70 mm) are 1 to 2 orders of magnitude smaller than the theoretical minimum length scale over which earthquake nucleation can occur. We propose that frictional stability and the transition between seismic and aseismic slip are modulated by local stressing rate and that fault sections, which would typically slip aseismically, may radiate seismic waves if they are rapidly stressed. Fault behavior of this type may provide physical insight into the mechanics of foreshocks, tremor, repeating earthquake sequences, and a minimum earthquake source dimension.

Activation of preexisting transverse structures in an evolving magmatic rift in East Africa

NASA Astrophysics Data System (ADS)

Muirhead, J. D.; Kattenhorn, S. A.

2018-01-01

Inherited crustal weaknesses have long been recognized as important factors in strain localization and basin development in the East African Rift System (EARS). However, the timing and kinematics (e.g., sense of slip) of transverse (rift-oblique) faults that exploit these weaknesses are debated, and thus the roles of inherited weaknesses at different stages of rift basin evolution are often overlooked. The mechanics of transverse faulting were addressed through an analysis of the Kordjya fault of the Magadi basin (Kenya Rift). Fault kinematics were investigated from field and remote-sensing data collected on fault and joint systems. Our analysis indicates that the Kordjya fault consists of a complex system of predominantly NNE-striking, rift-parallel fault segments that collectively form a NNW-trending array of en echelon faults. The transverse Kordjya fault therefore reactivated existing rift-parallel faults in ∼1 Ma lavas as oblique-normal faults with a component of sinistral shear. In all, these fault motions accommodate dip-slip on an underlying transverse structure that exploits the Aswa basement shear zone. This study shows that transverse faults may be activated through a complex interplay among magma-assisted strain localization, preexisting structures, and local stress rotations. Rather than forming during rift initiation, transverse structures can develop after the establishment of pervasive rift-parallel fault systems, and may exhibit dip-slip kinematics when activated from local stress rotations. The Kordjya fault is shown here to form a kinematic linkage that transfers strain to a newly developing center of concentrated magmatism and normal faulting. It is concluded that recently activated transverse faults not only reveal the effects of inherited basement weaknesses on fault development, but also provide important clues regarding developing magmatic and tectonic systems as young continental rift basins evolve.

The shallow boreholes at The AltotiBerina near fault Observatory (TABOO; northern Apennines of Italy)

NASA Astrophysics Data System (ADS)

Chiaraluce, L.; Collettini, C.; Cattaneo, M.; Monachesi, G.

2014-04-01

As part of an interdisciplinary research project, funded by the European Research Council and addressing the mechanics of weak faults, we drilled three 200-250 m-deep boreholes and installed an array of seismometers. The array augments TABOO (The AltotiBerina near fault ObservatOry), a scientific infrastructure managed by the Italian National Institute of Geophysics and Volcanology. The observatory, which consists of a geophysical network equipped with multi-sensor stations, is located in the northern Apennines (Italy) and monitors a large and active low-angle normal fault. The drilling operations started at the end of 2011 and were completed by July 2012. We instrumented the boreholes with three-component short-period (2 Hz) passive instruments at different depths. The seismometers are now fully operational and collecting waveforms characterised by a very high signal to noise ratio that is ideal for studying microearthquakes. The resulting increase in the detection capability of the seismic network will allow for a broader range of transients to be identified.

SSME fault monitoring and diagnosis expert system

NASA Technical Reports Server (NTRS)

Ali, Moonis; Norman, Arnold M.; Gupta, U. K.

1989-01-01

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

Understanding Earthquake Fault Systems Using QuakeSim Analysis and Data Assimilation Tools

NASA Technical Reports Server (NTRS)

Donnellan, Andrea; Parker, Jay; Glasscoe, Margaret; Granat, Robert; Rundle, John; McLeod, Dennis; Al-Ghanmi, Rami; Grant, Lisa

2008-01-01

We are using the QuakeSim environment to model interacting fault systems. One goal of QuakeSim is to prepare for the large volumes of data that spaceborne missions such as DESDynI will produce. QuakeSim has the ability to ingest distributed heterogenous data in the form of InSAR, GPS, seismicity, and fault data into various earthquake modeling applications, automating the analysis when possible. Virtual California simulates interacting faults in California. We can compare output from long time history Virtual California runs with the current state of strain and the strain history in California. In addition to spaceborne data we will begin assimilating data from UAVSAR airborne flights over the San Francisco Bay Area, the Transverse Ranges, and the Salton Trough. Results of the models are important for understanding future earthquake risk and for providing decision support following earthquakes. Improved models require this sensor web of different data sources, and a modeling environment for understanding the combined data.

Stability and performance of propulsion control systems with distributed control architectures and failures

NASA Astrophysics Data System (ADS)

Belapurkar, Rohit K.

Future aircraft engine control systems will be based on a distributed architecture, in which, the sensors and actuators will be connected to the Full Authority Digital Engine Control (FADEC) through an engine area network. Distributed engine control architecture will allow the implementation of advanced, active control techniques along with achieving weight reduction, improvement in performance and lower life cycle cost. The performance of a distributed engine control system is predominantly dependent on the performance of the communication network. Due to the serial data transmission policy, network-induced time delays and sampling jitter are introduced between the sensor/actuator nodes and the distributed FADEC. Communication network faults and transient node failures may result in data dropouts, which may not only degrade the control system performance but may even destabilize the engine control system. Three different architectures for a turbine engine control system based on a distributed framework are presented. A partially distributed control system for a turbo-shaft engine is designed based on ARINC 825 communication protocol. Stability conditions and control design methodology are developed for the proposed partially distributed turbo-shaft engine control system to guarantee the desired performance under the presence of network-induced time delay and random data loss due to transient sensor/actuator failures. A fault tolerant control design methodology is proposed to benefit from the availability of an additional system bandwidth and from the broadcast feature of the data network. It is shown that a reconfigurable fault tolerant control design can help to reduce the performance degradation in presence of node failures. A T-700 turbo-shaft engine model is used to validate the proposed control methodology based on both single input and multiple-input multiple-output control design techniques.

Fault Identification by Unsupervised Learning Algorithm

NASA Astrophysics Data System (ADS)

Nandan, S.; Mannu, U.

2012-12-01

Contemporary fault identification techniques predominantly rely on the surface expression of the fault. This biased observation is inadequate to yield detailed fault structures in areas with surface cover like cities deserts vegetation etc and the changes in fault patterns with depth. Furthermore it is difficult to estimate faults structure which do not generate any surface rupture. Many disastrous events have been attributed to these blind faults. Faults and earthquakes are very closely related as earthquakes occur on faults and faults grow by accumulation of coseismic rupture. For a better seismic risk evaluation it is imperative to recognize and map these faults. We implement a novel approach to identify seismically active fault planes from three dimensional hypocenter distribution by making use of unsupervised learning algorithms. We employ K-means clustering algorithm and Expectation Maximization (EM) algorithm modified to identify planar structures in spatial distribution of hypocenter after filtering out isolated events. We examine difference in the faults reconstructed by deterministic assignment in K- means and probabilistic assignment in EM algorithm. The method is conceptually identical to methodologies developed by Ouillion et al (2008, 2010) and has been extensively tested on synthetic data. We determined the sensitivity of the methodology to uncertainties in hypocenter location, density of clustering and cross cutting fault structures. The method has been applied to datasets from two contrasting regions. While Kumaon Himalaya is a convergent plate boundary, Koyna-Warna lies in middle of the Indian Plate but has a history of triggered seismicity. The reconstructed faults were validated by examining the fault orientation of mapped faults and the focal mechanism of these events determined through waveform inversion. The reconstructed faults could be used to solve the fault plane ambiguity in focal mechanism determination and constrain the fault orientations for finite source inversions. The faults produced by the method exhibited good correlation with the fault planes obtained by focal mechanism solutions and previously mapped faults.

Diagnostic Analyzer for Gearboxes (DAG): User's Guide. Version 3.1 for Microsoft Windows 3.1

NASA Technical Reports Server (NTRS)

Jammu, Vinay B.; Kourosh, Danai

1997-01-01

This documentation describes the Diagnostic Analyzer for Gearboxes (DAG) software for performing fault diagnosis of gearboxes. First, the user would construct a graphical representation of the gearbox using the gear, bearing, shaft, and sensor tools contained in the DAG software. Next, a set of vibration features obtained by processing the vibration signals recorded from the gearbox using a signal analyzer is required. Given this information, the DAG software uses an unsupervised neural network referred to as the Fault Detection Network (FDN) to identify the occurrence of faults, and a pattern classifier called Single Category-Based Classifier (SCBC) for abnormality scaling of individual vibration features. The abnormality-scaled vibration features are then used as inputs to a Structure-Based Connectionist Network (SBCN) for identifying faults in gearbox subsystems and components. The weights of the SBCN represent its diagnostic knowledge and are derived from the structure of the gearbox graphically presented in DAG. The outputs of SBCN are fault possibility values between 0 and 1 for individual subsystems and components in the gearbox with a 1 representing a definite fault and a 0 representing normality. This manual describes the steps involved in creating the diagnostic gearbox model, along with the options and analysis tools of the DAG software.

GONAF - A borehole Geophysical Observatory around the North Anatolian Fault in the Eastern Sea of Marmara

NASA Astrophysics Data System (ADS)

Bohnhoff, Marco; Dresen, Georg; Ceken, Ulubey; Tuba Kadarioglu, Filiz; Feyiz Kartal, Recai; Kilic, Tugbay; Nurlu, Murat; Yanik, Kenan; Acarel, Digdem; Bulut, Fatih; Ito, Hisao; Johnson, Wade; Malin, Peter Eric; Mencin, Dave

2017-04-01

The Marmara section of the North Anatolian Fault Zone (NAFZ) runs under water and is located less than 20 km from the 15-million-person population center of Istanbul at its eastern portion. Based on historical seismicity data, recurrence times forecast an impending magnitude M>7 earthquake for this region. The permanent GONAF Geophysical Observatory at the North Anatolian Fault has been installed around this section to help capture the seismic and strain activity preceding, during, and after such an anticipated event. The GONAF observatory is currently comprised of seven 300 m deep vertical seismic profiling stations and four collocated 100 m deep borehole strainmeters. Five of the stations are located on the land surrounding the Princes Islands segment below the eastern Sea of Marmara and two are on the near-fault Princes Islands south of Istanbul. The 300 m boreholes have 1, 2, and 15 Hz 3-C seismometers near their bottoms. Above this are vertical, 1 Hz, seismometers at 210, 140, and 70 m depths. The strainmeter boreholes are located within a few meters of the seismometer boreholes and contain horizontal strain tensor sensors and 2 Hz 3-C seismometers at their bottoms. This selection of instruments and depths was done so as to ensure high-precision and broad-frequency earthquake monitoring and vertical profiling, all under low-noise conditions. GONAF is the first ICDP-driven project with a primarily focus on long-term monitoring of fault-zone dynamics. It has already contributed to earthquake hazard studies in the Istanbul area in several ways. Combining GONAF recordings with existing regional seismic stations now allows monitoring of the NAFZ offshore Istanbul down to magnitudes M<0. GONAF also improves the resolution of earthquake hypocenters and source parameters, better defining local fault branches, their seismicity, and earthquake potential. Using its vertical distribution of sensors, it has directly measured depth-dependent seismic site-effects for ground shaking studies. GONAF is starting to address fundamental questions related to earthquake nucleation, rupture dynamics, temporal changes of material properties and strain.

Mining induced seismic event on an inactive fault in view of local surface and in mine underground networksS

NASA Astrophysics Data System (ADS)

Rudzinski, Lukasz; Lizurek, Grzegorz; Plesiewicz, Beata

2014-05-01

On 19th March 2013 tremor shook the surface of Polkowice town were "Rudna" mine is located. This event of ML=4.2 was third most powerful seismic event recorded in Legnica Głogów Copper District (LGCD). Citizens of the area reported that felt tremors were bigger and last longer than any other ones felt in last couple years. The event was studied with use of two different networks: underground network of "Rudna" mine and surface local network run by IGF PAS (LUMINEOS network). The first one is composed of 32 vertical seismometers at mining level, except 5 sensors placed in elevator shafts, seismometers location depth varies from 300 down to 1000 meters below surface. The seismometers used in this network are vertical short period Willmore MkII and MkIII sensors, with the frequency band from 1Hz to 100Hz. At the beginning of 2013th the local surface network of the Institute of Geophysics Polish Academy of Sciences (IGF PAS) with acronym LUMINEOS was installed under agreement with KGHM SA and "Rudna" mine officials. This network at the moment of the March 19th 2013 event was composed of 4 short-period one-second triaxial seismometers LE-3D/1s manufactured by Lenartz Electronics. Analysis of spectral parameters of the records from in mine seismic system and surface LUMINEOS network along with broadband station KSP record were carried out. Location of the event was close to the Rudna Główna fault zone, the nodal planes orientations determined with two different approaches were almost parallel to the strike of the fault. The mechanism solutions were also obtained in form of Full Moment Tensor inversion from P wave amplitude pulses of underground records and waveform inversion of surface network seismograms. Final results of the seismic analysis along with macroseismic survey and observed effects from the destroyed part of the mining panel indicate that the mechanism of the event was thrust faulting on inactive tectonic fault. The results confirm that the fault zones are the areas of higher risk, even in case of carefully taken mining operations.

Chemiresistive Graphene Sensors for Ammonia Detection.

PubMed

Mackin, Charles; Schroeder, Vera; Zurutuza, Amaia; Su, Cong; Kong, Jing; Swager, Timothy M; Palacios, Tomás

2018-05-09

The primary objective of this work is to demonstrate a novel sensor system as a convenient vehicle for scaled-up repeatability and the kinetic analysis of a pixelated testbed. This work presents a sensor system capable of measuring hundreds of functionalized graphene sensors in a rapid and convenient fashion. The sensor system makes use of a novel array architecture requiring only one sensor per pixel and no selector transistor. The sensor system is employed specifically for the evaluation of Co(tpfpp)ClO 4 functionalization of graphene sensors for the detection of ammonia as an extension of previous work. Co(tpfpp)ClO 4 treated graphene sensors were found to provide 4-fold increased ammonia sensitivity over pristine graphene sensors. Sensors were also found to exhibit excellent selectivity over interfering compounds such as water and common organic solvents. The ability to monitor a large sensor array with 160 pixels provides insights into performance variations and reproducibility-critical factors in the development of practical sensor systems. All sensors exhibit the same linearly related responses with variations in response exhibiting Gaussian distributions, a key finding for variation modeling and quality engineering purposes. The mean correlation coefficient between sensor responses was found to be 0.999 indicating highly consistent sensor responses and excellent reproducibility of Co(tpfpp)ClO 4 functionalization. A detailed kinetic model is developed to describe sensor response profiles. The model consists of two adsorption mechanisms-one reversible and one irreversible-and is shown capable of fitting experimental data with a mean percent error of 0.01%.

IAPSA 2 small-scale system specification

NASA Technical Reports Server (NTRS)

Cohen, Gerald C.; Torkelson, Thomas C.

1990-01-01

The details of a hardware implementation of a representative small scale flight critical system is described using Advanced Information Processing System (AIPS) building block components and simulated sensor/actuator interfaces. The system was used to study application performance and reliability issues during both normal and faulted operation.

77 FR 57041 - Airworthiness Directives; Thielert Aircraft Engines GmbH Models TAE 125-01, TAE 125-02-99, and...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-17

... channel B manifold air pressure sensor hose permeability was not always recognized as a fault by the FADEC... States, on the relationship between the national Government and the States, or on the distribution of...

A high-fidelity airbus benchmark for system fault detection and isolation and flight control law clearance

NASA Astrophysics Data System (ADS)

Goupil, Ph.; Puyou, G.

2013-12-01

This paper presents a high-fidelity generic twin engine civil aircraft model developed by Airbus for advanced flight control system research. The main features of this benchmark are described to make the reader aware of the model complexity and representativeness. It is a complete representation including the nonlinear rigid-body aircraft model with a full set of control surfaces, actuator models, sensor models, flight control laws (FCL), and pilot inputs. Two applications of this benchmark in the framework of European projects are presented: FCL clearance using optimization and advanced fault detection and diagnosis (FDD).

A Decentralized Adaptive Approach to Fault Tolerant Flight Control

NASA Technical Reports Server (NTRS)

Wu, N. Eva; Nikulin, Vladimir; Heimes, Felix; Shormin, Victor

2000-01-01

This paper briefly reports some results of our study on the application of a decentralized adaptive control approach to a 6 DOF nonlinear aircraft model. The simulation results showed the potential of using this approach to achieve fault tolerant control. Based on this observation and some analysis, the paper proposes a multiple channel adaptive control scheme that makes use of the functionally redundant actuating and sensing capabilities in the model, and explains how to implement the scheme to tolerate actuator and sensor failures. The conditions, under which the scheme is applicable, are stated in the paper.

Rocket Engine Health Management: Early Definition of Critical Flight Measurements

NASA Technical Reports Server (NTRS)

Christenson, Rick L.; Nelson, Michael A.; Butas, John P.

2003-01-01

The NASA led Space Launch Initiative (SLI) program has established key requirements related to safety, reliability, launch availability and operations cost to be met by the next generation of reusable launch vehicles. Key to meeting these requirements will be an integrated vehicle health management ( M) system that includes sensors, harnesses, software, memory, and processors. Such a system must be integrated across all the vehicle subsystems and meet component, subsystem, and system requirements relative to fault detection, fault isolation, and false alarm rate. The purpose of this activity is to evolve techniques for defining critical flight engine system measurements-early within the definition of an engine health management system (EHMS). Two approaches, performance-based and failure mode-based, are integrated to provide a proposed set of measurements to be collected. This integrated approach is applied to MSFC s MC-1 engine. Early identification of measurements supports early identification of candidate sensor systems whose design and impacts to the engine components must be considered in engine design.

Data acquisition for a real time fault monitoring and diagnosis knowledge-based system for space power system

NASA Technical Reports Server (NTRS)

Wilhite, Larry D.; Lee, S. C.; Lollar, Louis F.

1989-01-01

The design and implementation of the real-time data acquisition and processing system employed in the AMPERES project is described, including effective data structures for efficient storage and flexible manipulation of the data by the knowledge-based system (KBS), the interprocess communication mechanism required between the data acquisition system and the KBS, and the appropriate data acquisition protocols for collecting data from the sensors. Sensor data are categorized as critical or noncritical data on the basis of the inherent frequencies of the signals and the diagnostic requirements reflected in their values. The critical data set contains 30 analog values and 42 digital values and is collected every 10 ms. The noncritical data set contains 240 analog values and is collected every second. The collected critical and noncritical data are stored in separate circular buffers. Buffers are created in shared memory to enable other processes, i.e., the fault monitoring and diagnosis process and the user interface process, to freely access the data sets.

Extreme temperature robust optical sensor designs and fault-tolerant signal processing

DOEpatents

Riza, Nabeel Agha [Oviedo, FL; Perez, Frank [Tujunga, CA

2012-01-17

Silicon Carbide (SiC) probe designs for extreme temperature and pressure sensing uses a single crystal SiC optical chip encased in a sintered SiC material probe. The SiC chip may be protected for high temperature only use or exposed for both temperature and pressure sensing. Hybrid signal processing techniques allow fault-tolerant extreme temperature sensing. Wavelength peak-to-peak (or null-to-null) collective spectrum spread measurement to detect wavelength peak/null shift measurement forms a coarse-fine temperature measurement using broadband spectrum monitoring. The SiC probe frontend acts as a stable emissivity Black-body radiator and monitoring the shift in radiation spectrum enables a pyrometer. This application combines all-SiC pyrometry with thick SiC etalon laser interferometry within a free-spectral range to form a coarse-fine temperature measurement sensor. RF notch filtering techniques improve the sensitivity of the temperature measurement where fine spectral shift or spectrum measurements are needed to deduce temperature.

Detection and Modeling of High-Dimensional Thresholds for Fault Detection and Diagnosis

NASA Technical Reports Server (NTRS)

He, Yuning

2015-01-01

Many Fault Detection and Diagnosis (FDD) systems use discrete models for detection and reasoning. To obtain categorical values like oil pressure too high, analog sensor values need to be discretized using a suitablethreshold. Time series of analog and discrete sensor readings are processed and discretized as they come in. This task isusually performed by the wrapper code'' of the FDD system, together with signal preprocessing and filtering. In practice,selecting the right threshold is very difficult, because it heavily influences the quality of diagnosis. If a threshold causesthe alarm trigger even in nominal situations, false alarms will be the consequence. On the other hand, if threshold settingdoes not trigger in case of an off-nominal condition, important alarms might be missed, potentially causing hazardoussituations. In this paper, we will in detail describe the underlying statistical modeling techniques and algorithm as well as the Bayesian method for selecting the most likely shape and its parameters. Our approach will be illustrated by several examples from the Aerospace domain.

A robust data fusion scheme for integrated navigation systems employing fault detection methodology augmented with fuzzy adaptive filtering

NASA Astrophysics Data System (ADS)

Ushaq, Muhammad; Fang, Jiancheng

2013-10-01

Integrated navigation systems for various applications, generally employs the centralized Kalman filter (CKF) wherein all measured sensor data are communicated to a single central Kalman filter. The advantage of CKF is that there is a minimal loss of information and high precision under benign conditions. But CKF may suffer computational overloading, and poor fault tolerance. The alternative is the federated Kalman filter (FKF) wherein the local estimates can deliver optimal or suboptimal state estimate as per certain information fusion criterion. FKF has enhanced throughput and multiple level fault detection capability. The Standard CKF or FKF require that the system noise and the measurement noise are zero-mean and Gaussian. Moreover it is assumed that covariance of system and measurement noises remain constant. But if the theoretical and actual statistical features employed in Kalman filter are not compatible, the Kalman filter does not render satisfactory solutions and divergence problems also occur. To resolve such problems, in this paper, an adaptive Kalman filter scheme strengthened with fuzzy inference system (FIS) is employed to adapt the statistical features of contributing sensors, online, in the light of real system dynamics and varying measurement noises. The excessive faults are detected and isolated by employing Chi Square test method. As a case study, the presented scheme has been implemented on Strapdown Inertial Navigation System (SINS) integrated with the Celestial Navigation System (CNS), GPS and Doppler radar using FKF. Collectively the overall system can be termed as SINS/CNS/GPS/Doppler integrated navigation system. The simulation results have validated the effectiveness of the presented scheme with significantly enhanced precision, reliability and fault tolerance. Effectiveness of the scheme has been tested against simulated abnormal errors/noises during different time segments of flight. It is believed that the presented scheme can be applied to the navigation system of aircraft or unmanned aerial vehicle (UAV).

Seismic Velocity Structure across the Hayward Fault Zone Near San Leandro, California

NASA Astrophysics Data System (ADS)

Strayer, L. M.; Catchings, R.; Chan, J. H.; Richardson, I. S.; McEvilly, A.; Goldman, M.; Criley, C.; Sickler, R. R.

2017-12-01

In Fall 2016 we conducted the East Bay Seismic Investigation, a NEHRP-funded collaboration between California State University, East Bay and the United State Geological Survey. The study produced a large volume of seismic data, allowing us to examine the subsurface across the East Bay plain and hills using a variety of geophysical methods. We know of no other survey performed in the past that has imaged this area, at this scale, and with this degree of resolution. Initial models show that seismic velocities of the Hayward Fault Zone (HFZ), the East Bay plain, and the East Bay hills are illuminated to depths of 5-6 km. We used explosive sources at 1-km intervals along a 15-km-long, NE-striking ( 055°), seismic line centered on the HFZ. Vertical- and horizontal-component sensors were spaced at 100 m intervals along the entire profile, with vertical-component sensors at 20 m intervals across mapped or suspected faults. Preliminary seismic refraction tomography across the HFZ, sensu lato, (includes sub-parallel, connected, and related faults), shows that the San Leandro Block (SLB) is a low-velocity feature in the upper 1-3 km, with nearly the same Vp as the adjacent Great Valley sediments to the east, and low Vs values. In our initial analysis we can trace the SLB and its bounding faults (Hayward, Chabot) nearly vertically, to at least 2-4 km depth. Similarly, preliminary migrated reflection images suggest that many if not all of the peripheral reverse, strike-slip and oblique-slip faults of the wider HFZ dip toward the SLB, into a curtain of relocated epicenters that define the HFZ at depth, indicative of a `flower-structure'. Preliminary Vs tomography identifies another apparently weak zone at depth, located about 1.5 km east of the San Leandro shoreline, that may represent the northward continuation of the Silver Creek Fault. Centered 4 km from the Bay, there is a distinctive, 2 km-wide, uplifted, horst-like, high-velocity structure (both Vp & Vs) that bounds the SLB to the west, outboard of the HF. We acquired a 2-D shear-wave velocity results using the multichannel analysis of surface waves (MASW) method on Rayleigh waves generated along the seismic profile. Our MASW result shows 600m depth of investigation, and Vs100 results range from 228m/s to 335m/s at fault zones, which correspond to NEHRP site classification D.

Autonomous Sun-Direction Estimation Using Partially Underdetermined Coarse Sun Sensor Configurations

NASA Astrophysics Data System (ADS)

O'Keefe, Stephen A.

In recent years there has been a significant increase in interest in smaller satellites as lower cost alternatives to traditional satellites, particularly with the rise in popularity of the CubeSat. Due to stringent mass, size, and often budget constraints, these small satellites rely on making the most of inexpensive hardware components and sensors, such as coarse sun sensors (CSS) and magnetometers. More expensive high-accuracy sun sensors often combine multiple measurements, and use specialized electronics, to deterministically solve for the direction of the Sun. Alternatively, cosine-type CSS output a voltage relative to the input light and are attractive due to their very low cost, simplicity to manufacture, small size, and minimal power consumption. This research investigates using coarse sun sensors for performing robust attitude estimation in order to point a spacecraft at the Sun after deployment from a launch vehicle, or following a system fault. As an alternative to using a large number of sensors, this thesis explores sun-direction estimation techniques with low computational costs that function well with underdetermined sets of CSS. Single-point estimators are coupled with simultaneous nonlinear control to achieve sun-pointing within a small percentage of a single orbit despite the partially underdetermined nature of the sensor suite. Leveraging an extensive analysis of the sensor models involved, sequential filtering techniques are shown to be capable of estimating the sun-direction to within a few degrees, with no a priori attitude information and using only CSS, despite the significant noise and biases present in the system. Detailed numerical simulations are used to compare and contrast the performance of the five different estimation techniques, with and without rate gyro measurements, their sensitivity to rate gyro accuracy, and their computation time. One of the key concerns with reducing the number of CSS is sensor degradation and failure. In this thesis, a Modified Rodrigues Parameter based CSS calibration filter suitable for autonomous on-board operation is developed. The sensitivity of this method's accuracy to the available Earth albedo data is evaluated and compared to the required computational effort. The calibration filter is expanded to perform sensor fault detection, and promising results are shown for reduced resolution albedo models. All of the methods discussed provide alternative attitude, determination, and control system algorithms for small satellite missions looking to use inexpensive, small sensors due to size, power, or budget limitations.

Redundancy management for efficient fault recovery in NASA's distributed computing system

NASA Technical Reports Server (NTRS)

Malek, Miroslaw; Pandya, Mihir; Yau, Kitty

1991-01-01

The management of redundancy in computer systems was studied and guidelines were provided for the development of NASA's fault-tolerant distributed systems. Fault recovery and reconfiguration mechanisms were examined. A theoretical foundation was laid for redundancy management by efficient reconfiguration methods and algorithmic diversity. Algorithms were developed to optimize the resources for embedding of computational graphs of tasks in the system architecture and reconfiguration of these tasks after a failure has occurred. The computational structure represented by a path and the complete binary tree was considered and the mesh and hypercube architectures were targeted for their embeddings. The innovative concept of Hybrid Algorithm Technique was introduced. This new technique provides a mechanism for obtaining fault tolerance while exhibiting improved performance.

High Speed, High Temperature, Fault Tolerant Operation of a Combination Magnetic-Hydrostatic Bearing Rotor Support System for Turbomachinery

NASA Technical Reports Server (NTRS)

Jansen, Mark; Montague, Gerald; Provenza, Andrew; Palazzolo, Alan

2004-01-01

Closed loop operation of a single, high temperature magnetic radial bearing to 30,000 RPM (2.25 million DN) and 540 C (1000 F) is discussed. Also, high temperature, fault tolerant operation for the three axis system is examined. A novel, hydrostatic backup bearing system was employed to attain high speed, high temperature, lubrication free support of the entire rotor system. The hydrostatic bearings were made of a high lubricity material and acted as journal-type backup bearings. New, high temperature displacement sensors were successfully employed to monitor shaft position throughout the entire temperature range and are described in this paper. Control of the system was accomplished through a stand alone, high speed computer controller and it was used to run both the fault-tolerant PID and active vibration control algorithms.

Guidance, Navigation, and Control System Design in a Mass Reduction Exercise

NASA Technical Reports Server (NTRS)

Crain, Timothy; Begly, Michael; Jackson, Mark; Broome, Joel

2008-01-01

Early Orion GN&C system designs optimized for robustness, simplicity, and utilization of commercially available components. During the System Definition Review (SDR), all subsystems on Orion were asked to re-optimize with component mass and steady state power as primary design metrics. The objective was to create a mass reserve in the Orion point of departure vehicle design prior to beginning the PDR analysis cycle. The Orion GN&C subsystem team transitioned from a philosophy of absolute 2 fault tolerance for crew safety and 1 fault tolerance for mission success to an approach of 1 fault tolerance for crew safety and risk based redundancy to meet probability allocations of loss of mission and loss of crew. This paper will discuss the analyses, rationale, and end results of this activity regarding Orion navigation sensor hardware, control effectors, and trajectory design.

Hidden Markov models for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic J. (Inventor)

1995-01-01

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Hidden Markov models for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic J. (Inventor)

1993-01-01

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

An SVM-based solution for fault detection in wind turbines.

PubMed

Santos, Pedro; Villa, Luisa F; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús

2015-03-09

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

Preliminary photovoltaic arc-fault prognostic tests using sacrificial fiber optic cabling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Jay Dean; Blemel, Kenneth D.; Peter, Francis

2013-02-01

Through the New Mexico Small Business Assistance Program, Sandia National Laboratories worked with Sentient Business Systems, Inc. to develop and test a novel photovoltaic (PV) arc-fault detection system. The system operates by pairing translucent polymeric fiber optic sensors with electrical circuitry so that any external abrasion to the system or internal heating causes the fiber optic connection to fail or detectably degrade. A periodic pulse of light is sent through the optical path using a transmitter-receiver pair. If the receiver does not detect the pulse, an alarm is sounded and the PV system can be de-energized. This technology has themore » unique ability to prognostically determine impending failures to the electrical system in two ways: (a) the optical connection is severed prior to physical abrasion or cutting of PV DC electrical conductors, and (b) the polymeric fiber optic cable melts via Joule heating before an arc-fault is established through corrosion. Three arc-faults were created in different configurations found in PV systems with the integrated fiber optic system to determine the feasibility of the technology. In each case, the fiber optic cable was broken and the system annunciated the fault.« less

Plate-rate laboratory friction experiments reveal potential slip instability on weak faults

NASA Astrophysics Data System (ADS)

Ikari, M.; Kopf, A.

2016-12-01

In earthquake science, it is commonly assumed that earthquakes nucleate on strong patches or "asperities", and data from laboratory friction experiments indicate a tendency for unstable slip (exhibited as velocity-weakening frictional behavior) in strong geologic materials. However, an overwhelming amount of these experiments were conducted at driving velocities ranging from 0.1 µm/s to over 1 m/s. Less data exists for shearing experiments driven at slow velocities on the order of cm/yr (nm/s), approximating plate tectonic rates which represent the natural driving condition on plate boundary faults. Recent laboratory work using samples recovered from the Tohoku region at the Japan Trench, within the high coseismic slip region of the 2011 M9 Tohoku earthquake, showed that the fault is extremely weak with a friction coefficient < 0.2. At sliding velocities of at least 0.1 µm/s mostly velocity-strengthening friction is observed, which is favorable for stable creep, consistent with earlier work. However, shearing at an imposed rate of 8.5 cm/yr produced both velocity-weakening friction and discrete slow slip events, which are likely instances of frictional instabilities or quasi-instabilities. Here, we expand on the Tohoku experiment by conducting cm/yr friction experiments on natural gouges obtained from a variety of other major fault zones obtained by scientific drilling; these include the San Andreas Fault, Costa Rica subduction zone, Nankai Trough (Japan), Barbados subduction zone, Alpine Fault (New Zealand), southern Cascadia, and Woodlark Basin (Papua New Guinea). We focus here on weak fault materials having a friction coefficient of < 0.5. At conventional laboratory driving rates of 0.1-30 µm/s, velocity strengthening is common. However, at cm/yr driving rates we commonly observe velocity-weakening friction and slow slip events, with most samples exhibit both behaviors. These results demonstrate when fault samples are sheared at plate tectonic rates in the laboratory, which best replicates natural forcing conditions, a tendency for unstable slip is revealed. Thus, weak faults should not be considered frictionally stable, but have the ability to participate in earthquake rupture or generate events themselves.

Slip triggered on southern California faults by the 1992 Joshua Tree, Landers, and big bear earthquakes

USGS Publications Warehouse

Bodin, Paul; Bilham, Roger; Behr, Jeff; Gomberg, Joan; Hudnut, Kenneth W.

1994-01-01

Five out of six functioning creepmeters on southern California faults recorded slip triggered at the time of some or all of the three largest events of the 1992 Landers earthquake sequence. Digital creep data indicate that dextral slip was triggered within 1 min of each mainshock and that maximum slip velocities occurred 2 to 3 min later. The duration of triggered slip events ranged from a few hours to several weeks. We note that triggered slip occurs commonly on faults that exhibit fault creep. To account for the observation that slip can be triggered repeatedly on a fault, we propose that the amplitude of triggered slip may be proportional to the depth of slip in the creep event and to the available near-surface tectonic strain that would otherwise eventually be released as fault creep. We advance the notion that seismic surface waves, perhaps amplified by sediments, generate transient local conditions that favor the release of tectonic strain to varying depths. Synthetic strain seismograms are presented that suggest increased pore pressure during periods of fault-normal contraction may be responsible for triggered slip, since maximum dextral shear strain transients correspond to times of maximum fault-normal contraction.

Late Pleistocene, Holocene, and decadal constancy of slip-rate of the Doruneh strike-slip fault, Iran.

NASA Astrophysics Data System (ADS)

Walker, R. T.; Fattahi, M.; Mousavi, Z.; Pathier, E.; Sloan, R. A.; Talebian, M.; Thomas, A. L.; Walpersdorf, A.

2014-12-01

The Doruneh left-lateral strike-slip fault of NE Iran has a prominent expression in the landscape, showing that the fault is active in the late Quaternary. Existing estimates of its slip-rate vary, however, which has led to suggestions that it may exhibit temporal changes in activity. Using high-resolution optical satellite imagery we make reconstructions of displacement across four alluvial fans that cross the Doruneh fault, and determine the ages of these fans using luminescence dating, combined with U-series dating of pedogenic carbonates in one case. The four fans, which vary in age from 10-100 kyr, yield estimates of slip rate of ~2-3 mm/yr. We compare the average slip-rate measurements to the rate of accumulation of strain across the Doruneh fault using GPS and InSAR measurements, and find that the slip-rate is likely to have remained constant - within the uncertainty of our measurements - over the last ~100 ka. The slip-rate that we measure is consistent with the E-W left-lateral Doruneh fault accommodating N-S right-lateral faulting by 'bookshelf' faulting, with clockwise rotation about a vertical axis, in a similar manner to the Eastern California Shear Zone.

Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare

PubMed Central

Haque, Shah Ahsanul; Rahman, Mustafizur; Aziz, Syed Mahfuzul

2015-01-01

Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR). PMID:25884786

On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition

PubMed Central

Banos, Oresti; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

2012-01-01

The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise) imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered. PMID:22969386

On the use of sensor fusion to reduce the impact of rotational and additive noise in human activity recognition.

PubMed

Banos, Oresti; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

2012-01-01

The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise) imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered.

Structural character of the Ghost Dance fault, Yucca Mountain, Nevada

USGS Publications Warehouse

Spengler, R.W.; Braun, C.A.; Linden, R.M.; Martin, L.G.; Ross-Brown, D. M.; Blackburn, R.L.

1993-01-01

Detailed structural mapping of an area that straddles the southern part of the Ghost Dance Fault has revealed the presence of several additional subparallel to anastomosing faults. These faults, mapped at a scale of 1:240, are: 1) dominantly north trending, 2) present on both the upthrown and downthrown sides of the surface trace of the Ghost Dance fault, 3) near-vertical features that commonly offset strata down to the west by 3 to 6 m (10 to 20 ft), and 4) commonly spaced 15 to 46 m (50 to 150 ft) apart. The zone also exhibits a structural fabric, containing an abundance of northwest-trending fractures. The width of the zone appears to be at least 213 m (700 ft) near the southernmost boundary of the study area but remains unknown near the northern extent of the study area, where the width of the study area is only 183 m (600 ft).

Seismic hazard maps for Haiti

USGS Publications Warehouse

Frankel, Arthur; Harmsen, Stephen; Mueller, Charles; Calais, Eric; Haase, Jennifer

2011-01-01

We have produced probabilistic seismic hazard maps of Haiti for peak ground acceleration and response spectral accelerations that include the hazard from the major crustal faults, subduction zones, and background earthquakes. The hazard from the Enriquillo-Plantain Garden, Septentrional, and Matheux-Neiba fault zones was estimated using fault slip rates determined from GPS measurements. The hazard from the subduction zones along the northern and southeastern coasts of Hispaniola was calculated from slip rates derived from GPS data and the overall plate motion. Hazard maps were made for a firm-rock site condition and for a grid of shallow shear-wave velocities estimated from topographic slope. The maps show substantial hazard throughout Haiti, with the highest hazard in Haiti along the Enriquillo-Plantain Garden and Septentrional fault zones. The Matheux-Neiba Fault exhibits high hazard in the maps for 2% probability of exceedance in 50 years, although its slip rate is poorly constrained.

Displacement-length scaling of brittle faults in ductile shear.

PubMed

Grasemann, Bernhard; Exner, Ulrike; Tschegg, Cornelius

2011-11-01

Within a low-grade ductile shear zone, we investigated exceptionally well exposed brittle faults, which accumulated antithetic slip and rotated into the shearing direction. The foliation planes of the mylonitic host rock intersect the faults approximately at their centre and exhibit ductile reverse drag. Three types of brittle faults can be distinguished: (i) Faults developing on pre-existing K-feldspar/mica veins that are oblique to the shear direction. These faults have triclinic flanking structures. (ii) Wing cracks opening as mode I fractures at the tips of the triclinic flanking structures, perpendicular to the shear direction. These cracks are reactivated as faults with antithetic shear, extend from the parent K-feldspar/mica veins and form a complex linked flanking structure system. (iii) Joints forming perpendicular to the shearing direction are deformed to form monoclinic flanking structures. Triclinic and monoclinic flanking structures record elliptical displacement-distance profiles with steep displacement gradients at the fault tips by ductile flow in the host rocks, resulting in reverse drag of the foliation planes. These structures record one of the greatest maximum displacement/length ratios reported from natural fault structures. These exceptionally high ratios can be explained by localized antithetic displacement along brittle slip surfaces, which did not propagate during their rotation during surrounding ductile flow.

Displacement–length scaling of brittle faults in ductile shear

PubMed Central

Grasemann, Bernhard; Exner, Ulrike; Tschegg, Cornelius

2011-01-01

Within a low-grade ductile shear zone, we investigated exceptionally well exposed brittle faults, which accumulated antithetic slip and rotated into the shearing direction. The foliation planes of the mylonitic host rock intersect the faults approximately at their centre and exhibit ductile reverse drag. Three types of brittle faults can be distinguished: (i) Faults developing on pre-existing K-feldspar/mica veins that are oblique to the shear direction. These faults have triclinic flanking structures. (ii) Wing cracks opening as mode I fractures at the tips of the triclinic flanking structures, perpendicular to the shear direction. These cracks are reactivated as faults with antithetic shear, extend from the parent K-feldspar/mica veins and form a complex linked flanking structure system. (iii) Joints forming perpendicular to the shearing direction are deformed to form monoclinic flanking structures. Triclinic and monoclinic flanking structures record elliptical displacement–distance profiles with steep displacement gradients at the fault tips by ductile flow in the host rocks, resulting in reverse drag of the foliation planes. These structures record one of the greatest maximum displacement/length ratios reported from natural fault structures. These exceptionally high ratios can be explained by localized antithetic displacement along brittle slip surfaces, which did not propagate during their rotation during surrounding ductile flow. PMID:26806996

On the design of fault-tolerant robotic manipulator systems

NASA Technical Reports Server (NTRS)

Tesar, Delbert

1993-01-01

Robotic systems are finding increasing use in space applications. Many of these devices are going to be operational on board the Space Station Freedom. Fault tolerance has been deemed necessary because of the criticality of the tasks and the inaccessibility of the systems to maintenance and repair. Design for fault tolerance in manipulator systems is an area within robotics that is without precedence in the literature. In this paper, we will attempt to lay down the foundations for such a technology. Design for fault tolerance demands new and special approaches to design, often at considerable variance from established design practices. These design aspects, together with reliability evaluation and modeling tools, are presented. Mechanical architectures that employ protective redundancies at many levels and have a modular architecture are then studied in detail. Once a mechanical architecture for fault tolerance has been derived, the chronological stages of operational fault tolerance are investigated. Failure detection, isolation, and estimation methods are surveyed, and such methods for robot sensors and actuators are derived. Failure recovery methods are also presented for each of the protective layers of redundancy. Failure recovery tactics often span all of the layers of a control hierarchy. Thus, a unified framework for decision-making and control, which orchestrates both the nominal redundancy management tasks and the failure management tasks, has been derived. The well-developed field of fault-tolerant computers is studied next, and some design principles relevant to the design of fault-tolerant robot controllers are abstracted. Conclusions are drawn, and a road map for the design of fault-tolerant manipulator systems is laid out with recommendations for a 10 DOF arm with dual actuators at each joint.

Unbalance detection in rotor systems with active bearings using self-sensing piezoelectric actuators

NASA Astrophysics Data System (ADS)

Ambur, Ramakrishnan; Rinderknecht, Stephan

2018-03-01

Machines which are developed today are highly automated due to increased use of mechatronic systems. To ensure their reliable operation, fault detection and isolation (FDI) is an important feature along with a better control. This research work aims to achieve and integrate both these functions with minimum number of components in a mechatronic system. This article investigates a rotating machine with active bearings equipped with piezoelectric actuators. There is an inherent coupling between their electrical and mechanical properties because of which they can also be used as sensors. Mechanical deflection can be reconstructed from these self-sensing actuators from measured voltage and current signals. These virtual sensor signals are utilised to detect unbalance in a rotor system. Parameters of unbalance such as its magnitude and phase are detected by parametric estimation method in frequency domain. Unbalance location has been identified using hypothesis of localization of faults. Robustness of the estimates against outliers in measurements is improved using weighted least squares method. Unbalances are detected in a real test bench apart from simulation using its model. Experiments are performed in stationary as well as in transient case. As a further step unbalances are estimated during simultaneous actuation of actuators in closed loop with an adaptive algorithm for vibration minimisation. This strategy could be used in systems which aim for both fault detection and control action.

Exploration of Geothermal Natural Resources from Menengai Caldera at Naruku, Kenya

NASA Astrophysics Data System (ADS)

Patlan, E.; Wamalwa, A.; Thompson, L. E.; Kaip, G.; Velasco, A. A.

2011-12-01

The Menengai Caldera, a large, dormant volcano, lies near the city of Naruku, Kenya (0.20°S, 36.07°E) and presents a significant natural geothermal energy resource that will benefit local communities. Kenya continues to explore and exploit its only major energy resource: geothermal energy. The Geothermal Development Company (GDC) of Kenya and University of Texas at El Paso (UTEP) have initially deployed seven seismic stations to address the volcanic hazards and associated processes that occurs through the analysis of data collection from seismic sensors that record ground motion. Seven more sensors are planned to be deployed in Aug. 2011. In general, the internal state and activity of the caldera is an important component to the understanding of porosity of the fault system, which is derived from the magma movement of the hot spot, and for the exploitation of geothermal energy. We analyze data from March to May 2011 to investigate the role of earthquakes and faults in controlling the caldera processes, and we find 15 events occurred within the caldera. We will utilize the double difference earthquake location algorithm (HypoDD) to analyze the local events in order to find active faulting of the caldera and the possible location of the magma chamber. For future work, we will combine the exiting data with the new seismic station to image the location of the caldera magma chamber.

Signal processing and neural network toolbox and its application to failure diagnosis and prognosis

NASA Astrophysics Data System (ADS)

Tu, Fang; Wen, Fang; Willett, Peter K.; Pattipati, Krishna R.; Jordan, Eric H.

2001-07-01

Many systems are comprised of components equipped with self-testing capability; however, if the system is complex involving feedback and the self-testing itself may occasionally be faulty, tracing faults to a single or multiple causes is difficult. Moreover, many sensors are incapable of reliable decision-making on their own. In such cases, a signal processing front-end that can match inference needs will be very helpful. The work is concerned with providing an object-oriented simulation environment for signal processing and neural network-based fault diagnosis and prognosis. In the toolbox, we implemented a wide range of spectral and statistical manipulation methods such as filters, harmonic analyzers, transient detectors, and multi-resolution decomposition to extract features for failure events from data collected by data sensors. Then we evaluated multiple learning paradigms for general classification, diagnosis and prognosis. The network models evaluated include Restricted Coulomb Energy (RCE) Neural Network, Learning Vector Quantization (LVQ), Decision Trees (C4.5), Fuzzy Adaptive Resonance Theory (FuzzyArtmap), Linear Discriminant Rule (LDR), Quadratic Discriminant Rule (QDR), Radial Basis Functions (RBF), Multiple Layer Perceptrons (MLP) and Single Layer Perceptrons (SLP). Validation techniques, such as N-fold cross-validation and bootstrap techniques, are employed for evaluating the robustness of network models. The trained networks are evaluated for their performance using test data on the basis of percent error rates obtained via cross-validation, time efficiency, generalization ability to unseen faults. Finally, the usage of neural networks for the prediction of residual life of turbine blades with thermal barrier coatings is described and the results are shown. The neural network toolbox has also been applied to fault diagnosis in mixed-signal circuits.

On- and off-fault deformation associated with the September 2013 Mw7.7 Balochistan earthquake: implications for geologic slip rate measurements

USGS Publications Warehouse

Gold, Ryan D.; Reitman, Nadine G.; Briggs, Richard; Barnhart, William; Hayes, Gavin; Wilson, Earl M.

2015-01-01

The 24 September 2013 Mw7.7 Balochistan, Pakistan earthquake ruptured a ~ 200 km-long stretch of the Hoshab fault in southern Pakistan and produced the second-largest lateral surface displacement observed for a continental strike-slip earthquake. We remotely measured surface deformation associated with this event using high-resolution (0.5 m) pre- and post-event satellite optical imagery. We document left lateral, near-field, on-fault offsets (10 m from fault) using 309 laterally offset piercing points, such as streams, terrace risers, and roads. Peak near-field displacement is 13.6 + 2.5/− 3.4 m. We characterize off-fault deformation by measuring medium- (< 350 m from fault) and far-field (> 350 m from fault) displacement using manual (259 measurements) and automated image cross-correlation methods, respectively. Off-fault peak lateral displacement values are ~ 15 m and exceed on-fault displacement magnitudes for ~ 85% of the rupture length. Our observations suggest that for this rupture, coseismic surface displacement typically increases with distance away from the surface trace of the fault; however, nearly 100% of total surface displacement occurs within a few hundred meters of the primary fault trace. Furthermore, off-fault displacement accounts for, on average, 28% of the total displacement but exhibits a highly heterogeneous along-strike pattern. The best agreement between near-field and far-field displacements generally corresponds to the narrowest fault zone widths. Our analysis demonstrates significant and heterogeneous mismatches between on- and off-fault coseismic deformation, and we conclude that this phenomenon should be considered in hazard models based on geologically determined on-fault slip rates.

Jurassic faults of southwest Alabama and offshore areas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mink, R.M.; Tew, B.H.; Bearden, B.L.

1991-03-01

Four fault groups affecting Jurassic strata occur in the southwest and offshore Alabama areas. They include the regional basement rift trend, the regional peripheral fault trend, the Mobile graben fault system, and the Lower Mobile Bay fault system. The regional basement system rift and regional peripheral fault trends are distinct and rim the inner margin of the eastern Gulf Coastal Plain. The regional basement rift trend is genetically related to the breakup of Pangea and the opening of the Gulf of Mexico in the Late Triassic-Early Jurassic. This fault trend is thought to have formed contemporaneously with deposition of Latemore » Triassic-Early Jurassic Eagle Mills Formation and to displace pre-Mesozoic rocks. The regional peripheral fault trend consists of a group of en echelon extensional faults that are parallel or subparallel to regional strike of Gulf Coastal Plain strata and correspond to the approximate updip limit of thick Louann Salt. Nondiapiric salt features are associated with the trend and maximum structural development is exhibited in the Haynesville-Smackover section. No hydrocarbon accumulations have been documented in the pre-Jurassic strata of southwest and offshore Alabama. Productive hydrocarbon reservoirs occur in Jurassic strata along the trends of the fault groups, suggesting a significant relationship between structural development in the Jurassic and hydrocarbon accumulation. Hydrocarbon traps are generally structural or contain a major structural component and include salt anticlines, faulted salt anticlines, and extensional fault traps. All of the major hydrocarbon accumulations are associated with movement of the Louann Salt along the regional peripheral fault trend, the Mobile graben fault system, or the Lower Mobile Bay fault system.« less

On- and off-fault deformation associated with the September 2013 Mw 7.7 Balochistan earthquake: Implications for geologic slip rate measurements

NASA Astrophysics Data System (ADS)

Gold, Ryan D.; Reitman, Nadine G.; Briggs, Richard W.; Barnhart, William D.; Hayes, Gavin P.; Wilson, Earl

2015-10-01

The 24 September 2013 Mw7.7 Balochistan, Pakistan earthquake ruptured a ~ 200 km-long stretch of the Hoshab fault in southern Pakistan and produced the second-largest lateral surface displacement observed for a continental strike-slip earthquake. We remotely measured surface deformation associated with this event using high-resolution (0.5 m) pre- and post-event satellite optical imagery. We document left lateral, near-field, on-fault offsets (10 m from fault) using 309 laterally offset piercing points, such as streams, terrace risers, and roads. Peak near-field displacement is 13.6 + 2.5/- 3.4 m. We characterize off-fault deformation by measuring medium- (< 350 m from fault) and far-field (> 350 m from fault) displacement using manual (259 measurements) and automated image cross-correlation methods, respectively. Off-fault peak lateral displacement values are ~ 15 m and exceed on-fault displacement magnitudes for ~ 85% of the rupture length. Our observations suggest that for this rupture, coseismic surface displacement typically increases with distance away from the surface trace of the fault; however, nearly 100% of total surface displacement occurs within a few hundred meters of the primary fault trace. Furthermore, off-fault displacement accounts for, on average, 28% of the total displacement but exhibits a highly heterogeneous along-strike pattern. The best agreement between near-field and far-field displacements generally corresponds to the narrowest fault zone widths. Our analysis demonstrates significant and heterogeneous mismatches between on- and off-fault coseismic deformation, and we conclude that this phenomenon should be considered in hazard models based on geologically determined on-fault slip rates.

Evolution of Effluent Chemistry at Crystal Geyser, Green River, Utah

NASA Astrophysics Data System (ADS)

Han, W. S.; Park, E.; Choung, S.; Kim, C. Y.; Piao, J.; Han, G.

2016-12-01

Several cold-water geysers and springs are located adjacent to the Green River in Utah where two major east-west faults, the Little Grand Wash and the Salt Wash Graben faults, trend roughly parallel to each other. Among these springs and geysers is Crystal Geyser, located immediately north of the Little Grand Wash fault and approximately 6 km south of the town of Green River. In this study, the fluid mechanics of the regularly erupting Crystal Geyser was investigated by instrumenting its conduit with pressure, temperature, pH, EC, and dissolved oxygen sensors, measuring every 1 minute during and between eruptions. The single eruption cycle at Crystal geyser lasted over four days and was composed of four parts: Minor Eruption (mEP), Major Eruption (MEP), Aftershock Eruption (Ae) and Recharge (R). Current eruption patterns exhibit a bimodal distribution although previous measurements and anecdotal evidence suggests that this pattern was different prior to recent seismic activity. Based on chemical characteristics, the primary sourcing aquifers characterized to be both Entrada and Navajo Sandstones with minor contribution from Paradox Formation brine. Contemporaneously, dissolved ionic species vary 0-44% while transition from mEP, MEP and R even if the degree of changes was different from individual ion. Generally, Na+, K+, Cl- and SO42- regularly decrease at the onset and throughout the MEP. These species then increase in concentration during the mEP. Conversely, Ca2+, Mg2+, Fe2+ and Sr2+ decrease and increase in concentration during the MEP and mEP, respectively. Inverse geochemical modeling was conducted to characterize the contribution of Crystal geyser effluents from endmembers of Entrada Sandstone, Navajo Sandstone and Paradox Formations. Results of inverse modeling show that, during the mEP, the Navajo, Entrada and brine supply 62-65%, 33-36% and 1%, respectively. During the MEP, the contribution shifts to 53-56%, 42-45% and 1% for the Navajo, Entrada and Brine, respectively.

Aftershocks of the 2014 South Napa, California, Earthquake: Complex faulting on secondary faults

USGS Publications Warehouse

Hardebeck, Jeanne L.; Shelly, David R.

2016-01-01

We investigate the aftershock sequence of the 2014 MW6.0 South Napa, California, earthquake. Low-magnitude aftershocks missing from the network catalog are detected by applying a matched-filter approach to continuous seismic data, with the catalog earthquakes serving as the waveform templates. We measure precise differential arrival times between events, which we use for double-difference event relocation in a 3D seismic velocity model. Most aftershocks are deeper than the mainshock slip, and most occur west of the mapped surface rupture. While the mainshock coseismic and postseismic slip appears to have occurred on the near-vertical, strike-slip West Napa fault, many of the aftershocks occur in a complex zone of secondary faulting. Earthquake locations in the main aftershock zone, near the mainshock hypocenter, delineate multiple dipping secondary faults. Composite focal mechanisms indicate strike-slip and oblique-reverse faulting on the secondary features. The secondary faults were moved towards failure by Coulomb stress changes from the mainshock slip. Clusters of aftershocks north and south of the main aftershock zone exhibit vertical strike-slip faulting more consistent with the West Napa Fault. The northern aftershocks correspond to the area of largest mainshock coseismic slip, while the main aftershock zone is adjacent to the fault area that has primarily slipped postseismically. Unlike most creeping faults, the zone of postseismic slip does not appear to contain embedded stick-slip patches that would have produced on-fault aftershocks. The lack of stick-slip patches along this portion of the fault may contribute to the low productivity of the South Napa aftershock sequence.

Miocene stratigraphy and structure of Sabine Pass, West Cameron, and East Cameron outer continental shelf areas, Louisiana

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, S.Y.; Watkins, J.S.

Mapping of Miocene stratigraphy and structure of the Sabine Pass, West Cameron, and East Cameron areas of the western Louisiana outer continental shelf - based on over 1300 mi of seismic data on a 4-mi grid, paleotops from 60 wells, and logs from 35 wells - resulted in time-structure and isochron maps at six intervals from the upper Pliocene to lower Miocene. The most pronounced structural features are the fault systems, which trend east-northeast to east along the Miocene stratigraphic trend. Isolated normal faults with small displacements characterize the inner inner shelf, whereas interconnected faults with greater displacements characterize themore » outer inner shelf. The inner inner shelf faults exhibit little growth, but expansion across the interconnected outer inner shelf fault ranges up to 1 sec two-way traveltime. The interconnected faults belong to two structurally independent fault families. The innermost shelf faults appear to root in the sediment column. A third set of faults located in the Sabine Pass area trends north-south. This fault set is thought to be related to basement movement and/or basement structure. Very little salt is evident in the area. A single diapir is located in West Cameron Block 110 and vicinity. There is little evidence of deep salt. Overall sediment thickness probably exceeds 20,000 ft, with the middle Miocene accounting for 8000 ft.« less

Wind turbine fault detection and classification by means of image texture analysis

NASA Astrophysics Data System (ADS)

Ruiz, Magda; Mujica, Luis E.; Alférez, Santiago; Acho, Leonardo; Tutivén, Christian; Vidal, Yolanda; Rodellar, José; Pozo, Francesc

2018-07-01

The future of the wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly offshore to benefit stronger and more uniform wind conditions. The cost of operation and maintenance of offshore wind turbines is approximately 15-35% of the total cost. Of this, 80% goes towards unplanned maintenance issues due to different faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and challenges is by applying low-cost advanced fault detection schemes. This work proposes a new method for detection and classification of wind turbine actuators and sensors faults in variable-speed wind turbines. For this purpose, time domain signals acquired from the operating wind turbine are represented as two-dimensional matrices to obtain grayscale digital images. Then, the image pattern recognition is processed getting texture features under a multichannel representation. In this work, four types of texture characteristics are used: statistical, wavelet, granulometric and Gabor features. Next, the most significant ones are selected using the conditional mutual criterion. Finally, the faults are detected and distinguished between them (classified) using an automatic classification tool. In particular, a 10-fold cross-validation is used to obtain a more generalized model and evaluates the classification performance. Coupled non-linear aero-hydro-servo-elastic simulations of a 5 MW offshore type wind turbine are carried out in several fault scenarios. The results show a promising methodology able to detect and classify the most common wind turbine faults.

Field characterization of elastic properties across a fault zone reactivated by fluid injection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jeanne, Pierre; Guglielmi, Yves; Rutqvist, Jonny

In this paper, we studied the elastic properties of a fault zone intersecting the Opalinus Clay formation at 300 m depth in the Mont Terri Underground Research Laboratory (Switzerland). Four controlled water injection experiments were performed in borehole straddle intervals set at successive locations across the fault zone. A three-component displacement sensor, which allowed capturing the borehole wall movements during injection, was used to estimate the elastic properties of representative locations across the fault zone, from the host rock to the damage zone to the fault core. Young's moduli were estimated by both an analytical approach and numerical finite differencemore » modeling. Results show a decrease in Young's modulus from the host rock to the damage zone by a factor of 5 and from the damage zone to the fault core by a factor of 2. In the host rock, our results are in reasonable agreement with laboratory data showing a strong elastic anisotropy characterized by the direction of the plane of isotropy parallel to the laminar structure of the shale formation. In the fault zone, strong rotations of the direction of anisotropy can be observed. Finally, the plane of isotropy can be oriented either parallel to bedding (when few discontinuities are present), parallel to the direction of the main fracture family intersecting the zone, and possibly oriented parallel or perpendicular to the fractures critically oriented for shear reactivation (when repeated past rupture along this plane has created a zone).« less

Field characterization of elastic properties across a fault zone reactivated by fluid injection

DOE PAGES

Jeanne, Pierre; Guglielmi, Yves; Rutqvist, Jonny; ...

2017-08-12

In this paper, we studied the elastic properties of a fault zone intersecting the Opalinus Clay formation at 300 m depth in the Mont Terri Underground Research Laboratory (Switzerland). Four controlled water injection experiments were performed in borehole straddle intervals set at successive locations across the fault zone. A three-component displacement sensor, which allowed capturing the borehole wall movements during injection, was used to estimate the elastic properties of representative locations across the fault zone, from the host rock to the damage zone to the fault core. Young's moduli were estimated by both an analytical approach and numerical finite differencemore » modeling. Results show a decrease in Young's modulus from the host rock to the damage zone by a factor of 5 and from the damage zone to the fault core by a factor of 2. In the host rock, our results are in reasonable agreement with laboratory data showing a strong elastic anisotropy characterized by the direction of the plane of isotropy parallel to the laminar structure of the shale formation. In the fault zone, strong rotations of the direction of anisotropy can be observed. Finally, the plane of isotropy can be oriented either parallel to bedding (when few discontinuities are present), parallel to the direction of the main fracture family intersecting the zone, and possibly oriented parallel or perpendicular to the fractures critically oriented for shear reactivation (when repeated past rupture along this plane has created a zone).« less

Internal structure of the San Jacinto fault zone in the trifurcation area southeast of Anza, California, from data of dense seismic arrays

NASA Astrophysics Data System (ADS)

Qin, L.; Ben-Zion, Y.; Qiu, H.; Share, P.-E.; Ross, Z. E.; Vernon, F. L.

2018-04-01

We image the internal structure of the San Jacinto fault zone (SJFZ) in the trifurcation area southeast of Anza, California, with seismic records from dense linear and rectangular arrays. The examined data include recordings from more than 20 000 local earthquakes and nine teleseismic events. Automatic detection algorithms and visual inspection are used to identify P and S body waves, along with P- and S-types fault zone trapped waves (FZTW). The location at depth of the main branch of the SJFZ, the Clark fault, is identified from systematic waveform changes across lines of sensors within the dense rectangular array. Delay times of P arrivals from teleseismic and local events indicate damage asymmetry across the fault, with higher damage to the NE, producing a local reversal of the velocity contrast in the shallow crust with respect to the large-scale structure. A portion of the damage zone between the main fault and a second mapped surface trace to the NE generates P- and S-types FZTW. Inversions of high-quality S-type FZTW indicate that the most likely parameters of the trapping structure are width of ˜70 m, S-wave velocity reduction of 60 per cent, Q value of 60 and depth of ˜2 km. The local reversal of the shallow velocity contrast across the fault with respect to large-scale structure is consistent with preferred propagation of earthquake ruptures in the area to the NW.

Accommodating Sensor Bias in MRAC for State Tracking

NASA Technical Reports Server (NTRS)

Patre, Parag; Joshi, Suresh M.

2011-01-01

The problem of accommodating unknown sensor bias is considered in a direct model reference adaptive control (MRAC) setting for state tracking using state feedback. Sensor faults can occur during operation, and if the biased state measurements are directly used with a standard MRAC control law, neither closed-loop signal boundedness, nor asymptotic tracking can be guaranteed and the resulting tracking errors may be unbounded or unacceptably large. A modified MRAC law is proposed, which combines a bias estimator with control gain adaptation, and it is shown that signal boundedness can be accomplished, although the tracking error may not go to zero. Further, for the case wherein an asymptotically stable sensor bias estimator is available, an MRAC control law is proposed to accomplish asymptotic tracking and signal boundedness. Such a sensor bias estimator can be designed if additional sensor measurements are available, as illustrated for the case wherein bias is present in the rate gyro and airspeed measurements. Numerical example results are presented to illustrate each of the schemes.

Adaptive Control in the Presence of Simultaneous Sensor Bias and Actuator Failures

NASA Technical Reports Server (NTRS)

Joshi, Suresh M.

2012-01-01

The problem of simultaneously accommodating unknown sensor biases and unknown actuator failures in uncertain systems is considered in a direct model reference adaptive control (MRAC) setting for state tracking using state feedback. Sensor biases and actuator faults may be present at the outset or may occur at unknown instants of time during operation. A modified MRAC law is proposed, which combines sensor bias estimation with control gain adaptation for accommodation of sensor biases and actuator failures. This control law is shown to provide signal boundedness in the resulting system. For the case when an external asymptotically stable sensor bias estimator is available, an MRAC law is developed to accomplish asymptotic state tracking and signal boundedness. For a special case wherein biases are only present in the rate measurements and bias-free position measurements are available, an MRAC law is developed using a model-independent bias estimator, and is shown to provide asymptotic state tracking with signal boundedness.

New Constraints on Late Pleistocene - Holocene Slip Rates and Seismic Behavior Along the Panamint Valley Fault Zone, Eastern California

NASA Astrophysics Data System (ADS)

Hoffman, W.; Kirby, E.; McDonald, E.; Walker, J.; Gosse, J.

2008-12-01

Space-time patterns of seismic strain release along active fault systems can provide insight into the geodynamics of deforming lithosphere. Along the eastern California shear zone, fault systems south of the Garlock fault appear to have experienced an ongoing pulse of seismic activity over the past ca. 1 kyr (Rockwell et al., 2000). Recently, this cluster of seismicity has been implicated as both cause and consequence of the oft-cited discrepancy between geodetic velocities and geologic slip rates in this region (Dolan et al., 2007; Oskin et al., 2008). Whether other faults within the shear zone exhibit similar behavior remains uncertain. Here we report the preliminary results of new investigations of slip rates and seismic history along the Panamint Valley fault zone (PVFZ). The PVFZ is characterized by dextral, oblique-normal displacement along a moderately to shallowly-dipping range front fault. Previous workers (Zhang et al., 1990) identified a relatively recent surface rupture confined to a ~25 km segment of the southern fault zone and associated with dextral displacements of ~3 m. Our mapping reveals that youthful scarps ranging from 2-4 m in height are distributed along the central portion of the fault zone for at least 50 km. North of Ballarat, a releasing jog in the fault zone forms a 2-3 km long embayment. Displacement of debris-flow levees and channels along NE-striking faults that confirm that displacement is nearly dip-slip, consistent with an overall transport direction toward ~340°, and affording an opportunity to constrain fault displacement directly from the vertical offset of alluvial surfaces of varying age. At the mouth of Happy Canyon, the frontal fault strand displaces a fresh debris-flow by ~3-4 m; soil development atop the debris-flow surface is incipient to negligible. Radiocarbon ages from logs embedded in the flow matrix constrain the timing of the most recent event to younger than ~ 600 cal yr BP. Older alluvial surfaces, such as that buried by the debris-flow lobe, exhibit progressively larger displacement (up to 10-12 m). Well-preserved bar and swale morphology, incipient varnishing of surface boulders, and weak soil development all suggest that this surface is Late Holocene in age. We are working to confirm this inference, but if correct, it suggests that this fault system may have experienced ~3-4 events in the relatively recent past. Finally, preliminary surface ages from even older surfaces along this portion of the fault zone place limits on the slip rate over Late Pleistocene time. Cosmogenic 10Be surface clast dating of an alluvial surface with well-developed pavement and moderate soil development near Happy Canyon suggests a surface age of 30-35 kyr. We are working to refine this estimate with new dating and soil characterization, but our preliminary reconstructions of displacement of this surface across the two primary fault strands are consistent with slip rates that exceed ~3 mm/yr. Overall, these results are consistent with the inference that the Panamint Valley fault zone is the primary structure that accomplishes transfer of right-lateral shear across the Garlock Fault.

Intelligent Wireless Sensor Networks for System Health Monitoring

NASA Technical Reports Server (NTRS)

Alena, Rick

2011-01-01

Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network (PAN) standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. WSNs provide the inherent fault tolerance required for aerospace applications. The Discovery and Systems Health Group at NASA Ames Research Center has been developing WSN technology for use aboard aircraft and spacecraft for System Health Monitoring of structures and life support systems using funding from the NASA Engineering and Safety Center and Exploration Technology Development and Demonstration Program. This technology provides key advantages for low-power, low-cost ancillary sensing systems particularly across pressure interfaces and in areas where it is difficult to run wires. Intelligence for sensor networks could be defined as the capability of forming dynamic sensor networks, allowing high-level application software to identify and address any sensor that joined the network without the use of any centralized database defining the sensors characteristics. The IEEE 1451 Standard defines methods for the management of intelligent sensor systems and the IEEE 1451.4 section defines Transducer Electronic Datasheets (TEDS), which contain key information regarding the sensor characteristics such as name, description, serial number, calibration information and user information such as location within a vehicle. By locating the TEDS information on the wireless sensor itself and enabling access to this information base from the application software, the application can identify the sensor unambiguously and interpret and present the sensor data stream without reference to any other information. The application software is able to read the status of each sensor module, responding in real-time to changes of PAN configuration, providing the appropriate response for maintaining overall sensor system function, even when sensor modules fail or the WSN is reconfigured. The session will present the architecture and technical feasibility of creating fault-tolerant WSNs for aerospace applications based on our application of the technology to a Structural Health Monitoring testbed. The interim results of WSN development and testing including our software architecture for intelligent sensor management will be discussed in the context of the specific tradeoffs required for effective use. Initial certification measurement techniques and test results gauging WSN susceptibility to Radio Frequency interference are introduced as key challenges for technology adoption. A candidate Developmental and Flight Instrumentation implementation using intelligent sensor networks for wind tunnel and flight tests is developed as a guide to understanding key aspects of the aerospace vehicle design, test and operations life cycle.

Crowdsourced earthquake early warning.

PubMed

Minson, Sarah E; Brooks, Benjamin A; Glennie, Craig L; Murray, Jessica R; Langbein, John O; Owen, Susan E; Heaton, Thomas H; Iannucci, Robert A; Hauser, Darren L

2015-04-01

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M w (moment magnitude) 7 earthquake on California's Hayward fault, and real data from the M w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing.

Crowdsourced earthquake early warning

PubMed Central

Minson, Sarah E.; Brooks, Benjamin A.; Glennie, Craig L.; Murray, Jessica R.; Langbein, John O.; Owen, Susan E.; Heaton, Thomas H.; Iannucci, Robert A.; Hauser, Darren L.

2015-01-01

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an Mw (moment magnitude) 7 earthquake on California’s Hayward fault, and real data from the Mw 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing. PMID:26601167

Sensor for detecting changes in magnetic fields

DOEpatents

Praeg, W.F.

1980-02-26

A sensor is described for detecting changes in the magnetic field of the equilibrium-field coil of a Tokamak plasma device that comprises a pair of bifilar wires disposed circumferentially, one inside and one outside the equilibrium-field coil. Each is shorted at one end. The difference between the voltages detected at the other ends of the bifilar wires provides a measure of changing flux in the equilibrium-field coil. This difference can be used to detect faults in the coil in time to take action to protect the coil.